The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10–50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales.
Citation: Zhongzhe Ouyang, Ke Liu, Min Lu. Bias correction based on AR model in spurious regression[J]. AIMS Mathematics, 2024, 9(4): 8439-8460. doi: 10.3934/math.2024410
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The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10–50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales.
In this article, we study the following anisotropic singular →p(⋅)-Laplace equation
{−N∑i=1∂xi(|∂xiu|pi(x)−2∂xiu)=f(x)u−β(x)+g(x)uq(x) in Ω,u>0 in Ω,u=0 on ∂Ω, | (1.1) |
where Ω is a bounded domain in RN (N≥3) with smooth boundary ∂Ω; f∈L1(Ω) is a positive function; g∈L∞(Ω) is a nonnegative function; β∈C(¯Ω) such that 1<β(x)<∞ for any x∈¯Ω; q∈C(¯Ω) such that 0<q(x)<1 for any x∈¯Ω; pi∈C(¯Ω) such that 2≤pi(x)<N for any x∈¯Ω, i∈{1,...,N}.
The differential operator
N∑i=1∂xi(|∂xiu|pi(x)−2∂xiu), |
that appears in problem (1.1) is an anisotropic variable exponent →p(⋅)-Laplace operator, which represents an extension of the p(⋅)-Laplace operator
N∑i=1∂xi(|∂xiu|p(x)−2∂xiu), |
obtained in the case for each i∈{1,...,N}, pi(⋅)=p(⋅).
In the variable exponent case, p(⋅), the integrability condition changes with each point in the domain. This makes variable exponent Sobolev spaces very useful in modeling materials with spatially varying properties and in studying partial differential equations with non-standard growth conditions [1,2,3,4,5,6,7,8].
Anisotropy, on the other hand, adds another layer of complexity, providing a robust mathematical framework for modeling and solving problems that involve complex materials and phenomena exhibiting non-uniform and direction-dependent properties. This is represented mathematically by having different exponents for different partial derivatives. We refer to the papers [9,10,11,12,13,14,15,16,17,18,19,20,21] and references for further reading.
The progress in researching anisotropic singular problems with →p(⋅)-growth, however, has been relatively slow. There are only a limited number of studies available on this topic in academic literature. We could only refer to the papers [22,23,24] that were published recently. In [22], the author studied an anisotropic singular problems with constant case p(⋅)=p but with a variable singularity, where existence and regularity of positive solutions was obtained via the approximation methods. In [23], the author obtained the existence and regularity results of positive solutions by using the regularity theory and approximation methods. In [24], the authors showed the existence of positive solutions using the regularity theory and maximum principle. However, none of these papers studied combined effects of variable singular and sublinear nonlinearities.
We would also like to mention that the singular problems of the type
{−Δu=f(x)u−β in Ω,u>0 in Ω,u=0 on ∂Ω, | (1.2) |
have been intensively studied because of their wide applications to physical models in the study of non-Newtonian fluids, boundary layer phenomena for viscous fluids, chemical heterogenous catalysts, glacial advance, etc. (see, e.g., [25,26,27,28,29,30]).
These studies, however, have mainly focused on the case 0<β<1, i.e., the weak singularity (see, e.g. [31,32,33,34,35,36]), and in this case, the corresponding energy functional is continuous.
When β>1 (the strong singularity), on the other hand, the situation changes dramatically, and numerous challenges emerge in the analysis of differential equations of the type (1.2), where the primary challenge encountered is due to the lack of integrability of u−β for u∈H10(Ω) [37,38,39,40,41].
To overcome these challenges, as an alternative approach, the so-called "compatibility relation" between f(x) and β has been introduced in the recent studies [37,40,42]. This method, used along with a constrained minimization and the Ekeland's variational principle [43], suggests a practical approach to obtain solutions to the problems of the type (1.2). In the present paper, we generalize these results to nonstandard p(⋅)-growth.
The paper is organized as follows. In Section 2, we provide some fundamental information for the theory of variable Sobolev spaces since it is our work space. In Section 3, first we obtain the auxiliary results. Then, we present our main result and obtain a positive solution to problem (1.1). In Section 4, we provide an example to illustrate our results in a concrete way.
We start with some basic concepts of variable Lebesgue-Sobolev spaces. For more details, and the proof of the following propositions, we refer the reader to [1,2,44,45].
C+(¯Ω)={p;p∈C(¯Ω),infp(x)>1, for all x∈¯Ω}. |
For p∈C+(¯Ω) denote
p−:=infx∈¯Ωp(x)≤p(x)≤p+:=supx∈¯Ωp(x)<∞. |
For any p∈C+(¯Ω), we define the variable exponent Lebesgue space by
Lp(⋅)(Ω)={u∣u:Ω→R is measurable,∫Ω|u(x)|p(x)dx<∞}, |
then, Lp(⋅)(Ω) endowed with the norm
|u|p(⋅)=inf{λ>0:∫Ω|u(x)λ|p(x)dx≤1}, |
becomes a Banach space.
Proposition 2.1. For any u∈Lp(⋅)(Ω) and v∈Lp′(⋅)(Ω), we have
∫Ω|uv|dx≤C(p−,(p−)′)|u|p(⋅)|v|p′(⋅) |
where Lp′(x)(Ω) is the conjugate space of Lp(⋅)(Ω) such that 1p(x)+1p′(x)=1.
The convex functional Λ:Lp(⋅)(Ω)→R defined by
Λ(u)=∫Ω|u(x)|p(x)dx, |
is called modular on Lp(⋅)(Ω).
Proposition 2.2. If u,un∈Lp(⋅)(Ω) (n=1,2,...), we have
(i) |u|p(⋅)<1(=1;>1)⇔Λ(u)<1(=1;>1);
(ii) |u|p(⋅)>1⟹|u|p−p(⋅)≤Λ(u)≤|u|p+p(⋅);
(iii) |u|p(⋅)≤1⟹|u|p+p(⋅)≤Λ(u)≤|u|p−p(⋅);
(iv) limn→∞|un|p(⋅)=0⇔limn→∞Λ(un)=0;limn→∞|un|p(⋅)=∞⇔limn→∞Λ(un)=∞.
Proposition 2.3. If u,un∈Lp(⋅)(Ω) (n=1,2,...), then the following statements are equivalent:
(i) limn→∞|un−u|p(⋅)=0;
(ii) limn→∞Λ(un−u)=0;
(iii) un→u in measure in Ω and limn→∞Λ(un)=Λ(u).
The variable exponent Sobolev space W1,p(⋅)(Ω) is defined by
W1,p(⋅)(Ω)={u∈Lp(⋅)(Ω):|∇u|∈Lp(⋅)(Ω)}, |
with the norm
‖u‖1,p(⋅)=|u|p(⋅)+|∇u|p(⋅), |
or equivalently
‖u‖1,p(⋅)=inf{λ>0:∫Ω(|∇u(x)λ|p(x)+|u(x)λ|p(x))dx,≤1} |
for all u∈W1,p(⋅)(Ω).
As shown in [46], the smooth functions are in general not dense in W1,p(⋅)(Ω), but if the variable exponent p∈C+(¯Ω) is logarithmic Hölder continuous, that is
|p(x)−p(y)|≤−Mlog(|x−y|),for allx,y∈Ω such that|x−y|≤12, | (2.1) |
then the smooth functions are dense in W1,p(⋅)(Ω) and so the Sobolev space with zero boundary values, denoted by W1,p(⋅)0(Ω), as the closure of C∞0(Ω) does make sense. Therefore, the space W1,p(⋅)0(Ω) can be defined as ¯C∞0(Ω)‖⋅‖1,p(⋅)=W1,p(⋅)0(Ω), and hence, u∈W1,p(⋅)0(Ω) iff there exists a sequence (un) of C∞0(Ω) such that ‖un−u‖1,p(⋅)→0.
As a consequence of Poincaré inequality, ‖u‖1,p(⋅) and |∇u|p(⋅) are equivalent norms on W1,p(⋅)0(Ω) when p∈C+(¯Ω) is logarithmic Hölder continuous. Therefore, for any u∈W1,p(⋅)0(Ω), we can define an equivalent norm ‖u‖ such that
‖u‖=|∇u|p(⋅). |
Proposition 2.4. If 1<p−≤p+<∞, then the spaces Lp(⋅)(Ω) and W1,p(⋅)(Ω) are separable and reflexive Banach spaces.
Proposition 2.5. Let q∈C(¯Ω). If 1≤q(x)<p∗(x) for all x∈¯Ω, then the embedding W1,p(⋅)(Ω)↪Lq(⋅)(Ω) is compact and continuous, where
p∗(x)={Np(x)N−p(x),ifp(x)<N,+∞,ifp(x)≥N. |
Finally, we introduce the anisotropic variable exponent Sobolev spaces.
Let us denote by →p:¯Ω→RN the vectorial function →p(⋅)=(p1(⋅),...,pN(⋅)) with pi∈C+(¯Ω), i∈{1,...,N}. We will use the following notations.
Define →P+,→P−∈RN as
→P+=(p+1,...,p+N), →P−=(p−1,...,p−N), |
and P++,P+−,P−−∈R+ as
P++=max{p+1,...,p+N},P+−=max{p−1,...,p−N}, P−−=min{p−1,...,p−N}, |
Below, we use the definitions of the anisotropic variable exponent Sobolev spaces as given in [12] and assume that the domain Ω⊂RN satisfies all the necessary assumptions given in there.
The anisotropic variable exponent Sobolev space is defined by
W1,→p(⋅)(Ω)={u∈LP++(Ω):∂xiu∈Lpi(⋅)(Ω), i∈{1,...,N}}, |
which is associated with the norm
‖u‖W1,→p(⋅)(Ω)=|u|P++(⋅)+N∑i=1|∂xiu|pi(⋅). |
W1,→p(⋅)(Ω) is a reflexive Banach space under this norm.
The subspace W1,→p(⋅)0(Ω)⊂W1,→p(⋅)(Ω) consists of the functions that are vanishing on the boundary, that is,
W1,→p(⋅)0(Ω)={u∈W1,→p(⋅)(Ω):u=0on∂Ω}, |
We can define the following equivalent norm on W1,→p(⋅)0(Ω)
‖u‖→p(⋅)=N∑i=1|∂xiu|pi(⋅). |
since the smooth functions are dense in W1,→p(⋅)0(Ω), as the variable exponent pi∈C+(¯Ω), i∈{1,...,N} is logarithmic Hölder continuous.
The space W1,→p(⋅)0(Ω) is also a reflexive Banach space (for the theory of the anisotropic Sobolev spaces see, e.g., the monographs [2,47,48] and the papers [12,15]).
Throughout this article, we assume that
N∑i=11p−i>1, | (2.2) |
and define P∗−∈R+ and P−,∞∈R+ by
P∗−=N∑Ni=11p−i−1, P−,∞=max{P+−,P∗−}. |
Proposition 2.6. [[15], Theorem 1] Suppose that Ω⊂RN(N≥3) is a bounded domain with smooth boundary and relation (2.2) is fulfilled. For any q∈C(¯Ω) verifying
1<q(x)<P−,∞forallx∈¯Ω, |
the embedding
W1,→p(⋅)0(Ω)↪Lq(⋅)(Ω), |
is continuous and compact.
We define the singular energy functional J:W1,→p(⋅)0(Ω)→R corresponding to equation (1.1) by
J(u)=∫ΩN∑i=1|∂xiu|pi(x)pi(x)dx−∫Ωg(x)|u|q(x)+1q(x)+1dx+∫Ωf(x)|u|1−β(x)β(x)−1dx. |
Definition 3.1. A function u is called a weak solution to problem (1.1) if u∈W1,→p(⋅)0(Ω) such that u>0 in Ω and
∫Ω[N∑i=1|∂xiu|pi(x)−2∂xiu⋅∂xiφ−[g(x)uq(x)+f(x)u−β(x)]φ]dx=0, | (3.1) |
for all φ∈W1,→p(⋅)0(Ω).
Definition 3.2. Due to the singularity of J on W1,→p(⋅)0(Ω), we apply a constrained minimization for problem (1.1). As such, we introduce the following constrains:
N1={u∈W1,→p(⋅)0(Ω):∫Ω[N∑i=1|∂xiu|pi(x)−g(x)|u|q(x)+1−f(x)|u|1−β(x)]dx≥0}, |
and
N2={u∈W1,→p(⋅)0(Ω):∫Ω[N∑i=1|∂xiu|pi(x)−g(x)|u|q(x)+1−f(x)|u|1−β(x)]dx=0}. |
Remark 1. N2 can be considered as a Nehari manifold, even though in general it may not be a manifold. Therefore, if we set
c0:=infu∈N2J(u), |
then one might expect that c0 is attained at some u∈N2 (i.e., N2≠∅) and that u is a critical point of J.
Throughout the paper, we assume that the following conditions hold:
(A1) β:¯Ω→(1,∞) is a continuous function such that 1<β−≤β(x)≤β+<∞.
(A2) q:¯Ω→(0,1) is a continuous function such that 0<q−≤q(x)≤q+<1 and q++1≤β−.
(A3) 2≤P−−≤P++<P∗− for almost all x∈¯Ω.
(A4) f∈L1(Ω) is a positive function, that is, f(x)>0 a.e. in Ω.
(A5) g∈L∞(Ω) is a nonnegative function.
Lemma 3.3. For any u∈W1,→p(⋅)0(Ω) satisfying ∫Ωf(x)|u|1−β(x)dx<∞, the functional J is well-defined and coercive on W1,→p(⋅)0(Ω).
Proof. Denote by I1,I2 the indices sets I1={i∈{1,2,...,N}:|∂xiu|pi(⋅)≤1} and I2={i∈{1,2,...,N}:|∂xiu|pi(⋅)>1}. Using Proposition 2.2, it follows
|J(u)|≤1P−−N∑i=1∫Ω|∂xiu|pi(x)dx−|g|∞q++1∫Ω|u|q(x)+1dx+1β−−1∫Ωf(x)|u|1−β(x)dx≤1P−−(∑i∈I1|∂xiu|P−−pi(⋅)+∑i∈I2|∂xiu|P++pi(⋅))−|g|∞q++1min{|u|q++1q(x)+1,|u|q−+1q(x)+1}+1β−−1∫Ωf(x)|u|1−β(x)dx≤1P−−(N∑i=1|∂xiu|P++pi(⋅)+∑i∈I1|∂xiu|P−−pi(⋅))−|g|∞q++1min{|u|q++1q(x)+1,|u|q−+1q(x)+1}+1β−−1∫Ωf(x)|u|1−β(x)dx≤1P−−(N∑i=1|∂xiu|P++pi(⋅)+N)−|g|∞q++1min{|u|q++1q(x)+1,|u|q−+1q(x)+1}+1β−−1∫Ωf(x)|u|1−β(x)dx | (3.2) |
which shows that J is well-defined on W1,→p(⋅)0(Ω).
Applying similar steps and using the generalized mean inequality for ∑Ni=1|∂xiu|P−−pi(⋅) gives
J(u)≥1P++N∑i=1∫Ω|∂xiu|pi(x)dx−|g|∞q−+1∫Ω|u|q(x)+1dx+1β+−1∫Ωf(x)|u|1−β(x)dx≥1P++(∑i∈I1|∂xiu|P++pi(⋅)+∑i∈I2|∂xiu|P−−pi(⋅))−|g|∞q−+1∫Ω|u|q(x)+1dx+1β+−1∫Ωf(x)|u|1−β(x)dx≥NP++(‖u‖P−−→p(⋅)NP−−−1)−|g|∞q−+1‖u‖q++1→p(⋅)+1β+−1∫Ωf(x)|u|1−β(x)dx | (3.3) |
That is, J is coercive (i.e., J(u)→∞ as ‖u‖→p(⋅)→∞), and bounded below on W1,→p(⋅)0(Ω).
Next, we provide a-priori estimate.
Lemma 3.4. Assume that (un)⊂N1 is a nonnegative minimizing sequence for the minimization problem limn→∞J(un)=infN1J. Then, there are positive real numbers δ1,δ2 such that
δ1≤‖un‖→p(⋅)≤δ2 |
Proof. We assume by contradiction that there exists a subsequence (un) (not relabelled) such that un→0 in W1,→p(⋅)0(Ω). Thus, we can assume that ‖un‖→p(⋅)<1 for n large enough, and therefore, |∂xiun|Lpi(⋅)<1. Then, using Proposition 2.2, we have
∫ΩN∑i=1|∂xiun|pi(x)dx≤N∑i=1|∂xiun|p−ipi(⋅)≤N∑i=1|∂xiun|P−−pi(⋅) | (3.4) |
We recall the following elementary inequality: for all r,s>0 and m>0 it holds
rm+sm≤K(r+s)m | (3.5) |
where K:=max{1,21−m}. If we let r=|∂x1un|P−−Lp1(⋅), s=|∂x2un|P−−Lp2(⋅) and m=P−− in (3.5), it reads
|∂x1un|P−−Lp1(⋅)+|∂x2un|P−−Lp2(⋅)≤K(|∂x1un|Lp1(⋅)+|∂x2un|Lp2(⋅))P−− | (3.6) |
where K=max{1,21−P−−}=1. Applying this argument to the following terms in the sum ∑Ni=1|∂xiun|P−−pi(⋅) consecutively leads to
∫ΩN∑i=1|∂xiun|pi(x)dx≤N∑i=1|∂xiun|p−ipi(⋅)≤N∑i=1|∂xiun|P−−pi(⋅)≤(N∑i=1|∂xiun|pi(⋅))P−−≤‖un‖P−−→p(⋅) | (3.7) |
Now, using (3.7) and the reversed Hölder's inequality, we have
(∫Ωf(x)1/β−dx)β−(∫Ω|un|dx)1−β−≤∫Ωf(x)|un|1−β−dx≤∫Ωf(x)|un|1−β(x)dx | (3.8) |
By the assumption, (un)⊂N1. Thus, using (3.8) and Proposition 2.2 leads to
(∫Ωf(x)1/β−dx)β−(∫Ω|un|dx)1−β−≤∫Ωf(x)|un|1−β−dx≤‖un‖P−−→p(⋅)−|g|∞q−+1‖un‖q++1→0 | (3.9) |
Considering the assumption (A2), this can only happen if ∫Ω|un|dx→∞, which is not possible. Therefore, there exists a positive real number δ1 such that ‖un‖→p(⋅)≥δ1.
Now, let's assume, on the contrary, that ‖un‖→p(⋅)>1 for any n. We know, by the coerciveness of J, that the infimum of J is attained, that is, ∞<m:=infu∈W1,→p(⋅)0(Ω)J(u). Moreover, due to the assumption limn→∞J(un)=infN1J, (J(un)) is bounded. Then, applying the same steps as in (3.3), it follows
C‖un‖→p(⋅)+J(un)≥NP++(‖un‖P−−→p(⋅)NP−−−1)−|g|∞q−+1‖un‖q++1→p(⋅)+1β+−1∫Ωf(x)|un|1−β(x)dx |
for some constant C>0. If we drop the nonnegative terms, we obtain
C‖un‖→p(⋅)+J(un)≥1P++(‖un‖P−−→p(⋅)NP−−−1−N)−|g|∞q−+1‖u‖q++1→p(⋅) |
Dividing the both sides of the above inequality by ‖un‖q++1→p(⋅) and passing to the limit as n→∞ leads to a contradiction since we have q−+1<P−−. Therefore, there exists a positive real number δ2 such that ‖un‖→p(⋅)≤δ2.
Lemma 3.5. N1 is closed in W1,→p(⋅)0(Ω).
Proof. Assume that (un)⊂N1 such that un→ˆu(strongly) in W1,→p(⋅)0(Ω). Thus, un(x)→ˆu(x) a.e. in Ω, and ∂xiun→∂xiˆu in Lpi(⋅)(Ω) for i=1,2,...,N. Then, using Fatou's lemma, it reads
∫Ω[N∑i=1|∂xiun|pi(x)−g(x)|un|q(x)+1−f(x)|un|1−β(x)]dx≥0lim infn→∞[∫ΩN∑i=1|∂xiun|pi(x)dx]−∫Ωg(x)|ˆu|q(x)+1dx≥lim infn→∞[∫Ωf(x)|un|1−β(x)dx] |
and hence,
∫Ω[N∑i=1|∂xiˆu|pi(x)−g(x)|ˆu|q(x)+1−f(x)|ˆu|1−β(x)]dx≥0 |
which means ˆu∈N1. N1 is closed in W1,→p(⋅)0(Ω).
Lemma 3.6. For any u∈W1,→p(⋅)0(Ω) satisfying ∫Ωf(x)|u|1−β(x)dx<∞, there exists a unique continuous scaling function u∈W1,→p(⋅)0(Ω)→(0,∞):u⟼t(u) such that t(u)u∈N2, and t(u)u is the minimizer of the functional J along the ray {tu:t>0}, that is, inft>0J(tu)=J(t(u)u).
Proof. Fix u∈W1,→p(⋅)0(Ω) such that ∫Ωf(x)|u|1−β(x)dx<∞. For any t>0, the scaled functional, J(tu), determines a curve that can be characterized by
Φ(t):=J(tu),t∈[0,∞). | (3.10) |
Then, for a t∈[0,∞), tu∈N2 if and only if
Φ′(t)=ddtΦ(t)|t=t(u)=0. | (3.11) |
First, we show that Φ(t) attains its minimum on [0,∞) at some point t=t(u).
Considering the fact 0<∫Ωf(x)|u|1−β(x)dx<∞, we will examine two cases for t.
For 0<t<1:
Φ(t)=J(tu)≥tP++P++N∑i=1∫Ω|∂xiu|pi(x)dx−tq−+1q−+1∫Ωg(x)|u|q(x)+1dx+t1−β−β+−1∫Ωf(x)|u|1−β(x)dx:=Ψ0(t) |
Then, Ψ0:(0,1)→R is continuous. Taking the derivative of Ψ0 gives
Ψ′0(t)=tP++−1N∑i=1∫Ω|∂xiu|pi(x)dx−tq−∫Ωg(x)|u|q(x)+1dx+(1−β−β+−1)t−β−∫Ωf(x)|u|1−β(x)dx | (3.12) |
It is easy to see from (3.12) that Ψ′0(t)<0 when t>0 is small enough. Therefore, Ψ0(t) is decreasing when t>0 is small enough. In the same way,
Φ(t)=J(tu)≤tP−−P−−N∑i=1∫Ω|∂xiu|pi(x)dx−tq++1q++1∫Ωg(x)|u|q(x)+1dx+t1−β+β−−1∫Ωf(x)|u|1−β(x)dx:=Ψ1(t) |
Then, Ψ1:(0,1)→R is continuous. Taking the derivative of Ψ1 gives
Ψ′1(t)=tP−−−1N∑i=1∫Ω|∂xiu|pi(x)dx−tq+∫Ωg(x)|u|q(x)+1dx+(1−β+β+−1)t−β+∫Ωf(x)|u|1−β(x)dx | (3.13) |
But (3.13) also suggests that Ψ′1(t)<0 when t>0 is small enough. Thus, Ψ1(t) is decreasing when t>0 is small enough. Therefore, since Ψ0(t)≤Φ(t)≤Ψ1(t) for 0<t<1, Φ(t) is decreasing when t>0 is small enough.
For t>1: Following the same arguments shows that Ψ′0(t)>0 and Ψ′1(t)>0 when t>1 is large enough, and therefore, both Ψ0(t) and Ψ1(t) are increasing. Thus, Φ(t) is increasing when t>1 is large enough. In conclusion, since Φ(0)=0, Φ(t) attains its minimum on [0,∞) at some point, say t=t(u). That is, ddtΦ(t)|t=t(u)=0. Then, t(u)u∈N2 and inft>0J(tu)=J(t(u)u).
Next, we show that scaling function t(u) is continuous on W1,→p(⋅)0(Ω).
Let un→u in W1,→p(⋅)0(Ω)∖{0}, and tn=t(un). Then, by the definition, tnun∈N2. Defined in this way, the sequence tn is bounded. Assume on the contrary that tn→∞ (up to a subsequence). Then, using the fact tnun∈N2 it follows
∫ΩN∑i=1|∂xitnun|pi(x)dx−∫Ωg(x)|tnun|q(x)+1dx=∫Ωf(x)|tnun|1−β(x)dxtP−−n∫ΩN∑i=1|∂xiun|pi(x)dx−tq−+1n∫Ωg(x)|un|q(x)+1dx≤t1−β−n∫Ωf(x)|un|1−β(x)dx |
which suggests a contradiction when tn→∞. Hence, sequence tn is bounded. Therefore, there exists a subsequence tn (not relabelled) such that tn→t0, t0≥0. On the other hand, from Lemma 3.4, ‖tnun‖→p(⋅)≥δ1>0. Thus, t0>0 and t0u∈N2. By the uniqueness of the map t(u), t0=t(u), which concludes the continuity of t(u). In conclusion, for any ∈W1,→p(⋅)0(Ω) satisfying ∫Ωf(x)|u|1−β(x)dx<∞, the function t(u) scales u∈W1,→p(⋅)0(Ω) continuously to a point such that t(u)u∈N2.
Lemma 3.7. Assume that (un)⊂N1 is the nonnegative minimizing sequence for the minimization problem limn→∞J(un)=infN1J. Then, there exists a subsequence (un) (not relabelled) such that un→u∗ (strongly) in W1,→p(⋅)0(Ω).
Proof. Since (un) is bounded in W1,→p(⋅)0(Ω) and W1,→p(⋅)0(Ω) is reflexive, there exists a subsequence (un), not relabelled, and u∗∈W1,→p(⋅)0(Ω) such that
● un⇀u∗ (weakly) in W1,→p(⋅)0(Ω),
● un→u∗ in Ls(⋅)(Ω), 1<s(x)<P−,∞, for all x∈¯Ω,
● un(x)→u∗(x) a.e. in Ω.
Since the norm ‖⋅‖→p(⋅) is a continuous convex functional, it is weakly lower semicontinuous. Using this fact along with the Fatou's lemma, and Lemma 3.4, it reads
infN1J=limn→∞J(un)≥lim infn→∞[∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx]−∫Ωg(x)|u∗|q(x)+1q(x)+1dx+lim infn→∞[∫Ωf(x)|un|1−β(x)β(x)−1dx]≥∫ΩN∑i=1|∂xiu∗|pi(x)pi(x)dx−∫Ωg(x)|u∗|q(x)+1q(x)+1dx+∫Ωf(x)|u∗|1−β(x)β(x)−1dx=J(u∗)≥J(t(u∗)u∗)≥infN2J≥infN1J | (3.14) |
The above result implies, up to subsequences, that
limn→∞‖un‖→p(⋅)=‖u∗‖→p(⋅). | (3.15) |
Thus, (3.15) along with un⇀u∗ in W1,→p(⋅)0(Ω) show that un→u∗ in W1,→p(⋅)0(Ω).
The following is the main result of the present paper.
Theorem 3.8. Assume that the conditions (A1)−(A5) hold. Then, problem (1.1) has at least one positive W1,→p(⋅)0(Ω)-solution if and only if there exists ¯u∈W1,→p(⋅)0(Ω) satisfying ∫Ωf(x)|¯u|1−β(x)dx<∞.
Proof. (⇒): Assume that the function u∈W1,→p(⋅)0(Ω) is a weak solution to problem (1.1). Then, letting u=φ in Definition (3.1) gives
∫Ωf(x)|u|1−β(x)dx=∫ΩN∑i=1|∂xiu|pi(x)dx−∫Ωg(x)|u|q(x)+1dx≤‖u‖PM→p(⋅)−|g|∞|u|qMq(x)+1≤‖u‖PM→p(⋅)<∞, |
where PM:=max{P−−,P++} and qM:=max{q−,q+}, changing according to the base.
(⇐): Assume that there exists ¯u∈W1,→p(⋅)0(Ω) such that ∫Ωf(x)|¯u|1−β(x)dx<∞. Then, by Lemma 3.6, there exists a unique number t(¯u)>0 such that t(¯u)¯u∈N2.
The information we have had about J so far and the closeness of N1 allow us to apply Ekeland's variational principle to the problem infN1J. That is, it suggests the existence of a corresponding minimizing sequence (un)⊂N1 satisfying the following:
(E1) J(un)−infN1J≤1n,
(E2) J(un)−J(ν)≤1n‖un−ν‖→p(⋅),∀ν∈N1.
Due to the fact J(|un|)=J(un), it is not wrong to assume that un≥0 a.e. in Ω. Additionally, considering that (un)⊂N1 and following the same approach as it is done in the (⇒) part, we can obtain that ∫Ωf(x)|un|1−β(x)dx<∞. If all this information and the assumptions (A1), (A2) are taken into consideration, it follows that un(x)>0 a.e. in Ω.
The rest of the proof is split into two cases.
Case Ⅰ: (un)⊂N1∖N2 for n large.
For a function φ∈W1,→p(⋅)0(Ω) with φ≥0, and t>0, we have
0<(un(x)+tφ(x))1−β(x)≤un(x)1−β(x)a.e. inΩ. |
Therefore, using (A1), (A2) gives
∫Ωf(x)(un+tφ)1−β(x)dx≤∫Ωf(x)u1−β(x)ndx≤∫ΩN∑i=1|∂xiun|pi(x)dx−∫Ωg(x)uq(x)+1ndx<∞ | (3.16) |
Then, when t>0 is small enough in (3.16), we obtain
∫Ωf(x)(un+tφ)1−β(x)dx≤∫ΩN∑i=1|∂xi(un+tφ)|pi(x)dx−∫Ωg(x)(un+tφ)q(x)+1dx | (3.17) |
which means that ν:=un+tφ∈N1. Now, using (E2), it reads
1n‖tφ‖→p(⋅)≥J(un)−J(ν)=∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx−∫ΩN∑i=1|∂xi(un+tφ)|pi(x)pi(x)dx−∫Ωg(x)uq(x)+1nq(x)+1dx+∫Ωg(x)(un+tφ)q(x)+1q(x)+1dx+∫Ωf(x)u1−β(x)nβ(x)−1dx−∫Ωf(x)(un+tφ)1−β(x)β(x)−1dx |
Dividing the above inequality by t and passing to the infimum limit as t→0 gives
lim inft→0‖φ‖→p(⋅)n+lim inft→0[∫ΩN∑i=1[|∂xi(un+tφ)|pi(x)−|∂xiun|pi(x)]tpi(x)dx]⏟:=I1−lim inft→0[∫Ωg(x)[(un+tφ)q(x)+1−uq(x)+1n]t(q(x)+1)dx]⏟:=I2≥lim inft→0[∫Ωf(x)[(un+tφ)1−β(x)−u1−β(x)n]t(1−β(x))dx]⏟:=I3 |
Calculation of I1,I2 gives
I1=ddt(∫ΩN∑i=1|∂xi(un+tφ)|pi(x)pi(x)dx)|t=0=∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx | (3.18) |
and
I2=ddt(∫Ωg(x)(un+tφ)q(x)+1q(x)+1dx)|t=0=∫Ωg(x)uq(x)nφdx. | (3.19) |
For I3: Since for t>0 it holds
u1−β(x)n(x)−(un(x)+tφ(x))1−β(x)≥0,a.e. inΩ |
we can apply Fatou's lemma, that is,
I2=lim inft→0∫Ωf(x)[(un+tφ)1−β(x)−u1−β(x)n]t(1−β(x))dx≥∫Ωlim inft→0f(x)[(un+tφ)1−β(x)−u1−β(x)n]t(1−β(x))dx≥∫Ωf(x)u−β(x)nφdx | (3.20) |
Now, substituting I1,I2,I3 gives
‖φ‖→p(⋅)n+∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx−∫Ωg(x)uq(x)nφdx≥∫Ωf(x)u−β(x)nφdx |
From Lemma 3.7, we know that un→u∗ in W1,→p(⋅)0(Ω). Thus, also considering Fatou's lemma, we obtain
∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiφdx−∫Ωg(x)(u∗)q(x)φdx−∫Ωf(x)(u∗)−β(x)φdx≥0, | (3.21) |
for any φ∈W1,→p(⋅)0(Ω) with φ≥0. Letting φ=u∗ in (3.21) shows clearly that u∗∈N1.
Lastly, from Lemma 3.7, we can conclude that
limn→∞J(un)=J(u∗)=infN2J, |
which means
u∗∈N2,(witht(u∗)=1) | (3.22) |
Case Ⅱ: There exists a subsequence of (un) (not relabelled) contained in N2.
For a function φ∈W1,p(x)0(Ω) with φ≥0, t>0, and un∈N2, we have
∫Ωf(x)(un+tφ)1−β(x)dx≤∫Ωf(x)u1−β(x)ndx=∫ΩN∑i=1|∂xiu|pi(x)dx−∫Ωg(x)uq(x)+1ndx<∞, | (3.23) |
and hence, there exists a unique continuous scaling function, denoted by θn(t):=t(un+tφ)>0, corresponding to (un+tφ) so that θn(t)(un+tφ)∈N2 for n=1,2,.... Obviously, θn(0)=1. Since θn(t)(un+tφ)∈N2, we have
0=∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)dx−∫Ωg(x)(θn(t)(un+tφ))q(x)+1dx−∫Ωf(x)(θn(t)(un+tφ))1−β(x)dx≥∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)dx−θqM+1n(t)∫Ωg(x)(un+tφ)q(x)+1dx−θ1−βmn(t)∫Ωf(x)(un+tφ)1−β(x)dx, | (3.24) |
and
0=∫ΩN∑i=1|∂xiun|pi(x)dx−∫Ωg(x)uq(x)+1ndx−∫Ωf(x)u1−β(x)ndx. | (3.25) |
where βm:=min{β−,β+}. Then, using (3.24) and (3.25) together gives
0≥[−(q++1)[θn(0)+τ1(θn(t)−θn(0))]qm∫Ωg(x)(un+tφ)q(x)+1dx−(1−βm)[θn(0)+τ2(θn(t)−θn(0))]−βm∫Ωf(x)(un+tφ)1−β(x)dx](θn(t)−θn(0))+∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)dx−∫ΩN∑i=1|∂xi(un+tφ)|pi(x)dx+∫ΩN∑i=1|∂xi(un+tφ)|pi(x)dx−∫ΩN∑i=1|∂xiun|pi(x)dx−[∫Ωg(x)(un+tφ)q(x)+1dx−∫Ωg(x)uq(x)+1ndx]−[∫Ωf(x)(un+tφ)1−β(x)dx−∫Ωf(x)u1−β(x)ndx] | (3.26) |
for some constants τ1,τ2∈(0,1). To proceed, we assume that θ′n(0)=ddtθn(t)|t=0∈[−∞,∞]. In case this limit does not exist, we can consider a subsequence tk>0 of t such that tk→0 as k→∞.
Next, we show that θ′n(0)≠∞.
Dividing the both sides of (3.26) by t and passing to the limit as t→0 leads to
0≥[P−−∫ΩN∑i=1|∂xiun|pi(x)dx+(βm−1)∫Ωf(x)u1−β(x)ndx−(q++1)∫Ωg(x)uq(x)+1ndx]θ′n(0)+P−−∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx−(q++1)∫Ωg(x)uq(x)nφdx+(βm−1)∫Ωf(x)u−β(x)nφdx | (3.27) |
or
0≥[(P−−−q+−1)∫ΩN∑i=1|∂xiun|pi(x)dx+(βm+q+)∫Ωf(x)u1−β(x)ndx]θ′n(0)+P−−∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx−(q++1)∫Ωg(x)uq(x)nφdx+(βm−1)∫Ωf(x)u−β(x)nφdx | (3.28) |
which, along with Lemma 3.4, concludes that −∞≤θ′n(0)<∞, and hence, θ′n(0)≤¯c, uniformly in all large n.
Next, we show that θ′n(0)≠−∞.
First, we apply Ekeland's variational principle to the minimizing sequence (un)⊂N2(⊂N1). Thus, letting ν:=θn(t)(un+tφ) in (E2) gives
1n[|θn(t)−1|‖un‖→p(⋅)+tθn(t)‖φ‖→p(⋅)]≥J(un)−J(θn(t)(un+tφ))=∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx−∫Ωg(x)uq(x)+1nq(x)+1dx+∫Ωf(x)u1−β(x)nβ(x)−1dx−∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)pi(x)dx+∫Ωg(x)[θn(t)(un+tφ)]q(x)+1q(x)+1dx−∫Ωf(x)[θn(t)(un+tφ)]1−β(x)β(x)−1dx≥∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx−∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)pi(x)dx−∫Ωg(x)uq(x)+1nq(x)+1dx+∫Ωg(x)[θn(t)(un+tφ)]q(x)+1q(x)+1dx−1β−−1∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)dx | (3.29) |
If we use Lemma 3.4 to manipulate the norm ‖u+tφ‖→p(⋅), the integral in the last line of (3.29) can be written as follows
1β−−1∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)dx≤θPMn(t)β−−1∫ΩN∑i=1|∂xi(un+tφ)|pi(x)dx≤θPMn(t)β−−1‖un+tφ‖PM→p(⋅)≤2P++−1θPMn(t)CPM(δ2)‖φ‖PM→p(⋅)β−−1t | (3.30) |
Then,
1n[|θn(t)−1|‖un‖→p(⋅)+tθn(t)‖φ‖→p(⋅)]+∫ΩN∑i=1[|∂xi(un+tφ)|pi(x)−|∂xiun|pi(x)]pi(x)dx+2P++−1θPMn(t)CPM(δ2)‖φ‖PM→p(⋅)β−−1t≥[(1q−+1)[θn(0)+τ1(θn(t)−θn(0))]qm∫Ωg(x)(un+tφ)q(x)+1dx](θn(t)−θn(0))≥−∫ΩN∑i=1[|∂xiθn(t)(un+tφ)|pi(x)−|∂xi(un+tφ)|pi(x)]pi(x)dx+1q−+1∫Ωg(x)[(un+tφ)q(x)+1−uq(x)+1n]dx | (3.31) |
Dividing by t and passing to the limit as t→0 gives
1n‖φ‖→p(⋅)+2P++−1θPMn(t)CPM(δ2)‖φ‖PM→p(⋅)β−−1≥[(−1+1q−+1)∫ΩN∑i=1|∂xiun|pi(x)dx−1q−+1∫Ωf(x)u1−β(x)ndx−‖un‖→p(⋅)nsgn[θn(t)−1]]θ′n(0)−∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx+∫Ωg(x)uq(x)ndx | (3.32) |
which concludes that θ′n(0)≠−∞. Thus, θ′n(0)≥c_ uniformly in large n.
In conclusion, there exists a constant, C0>0 such that |θ′n(0)|≤C0 when n≥N0,N0∈N.
Next, we show that u∗∈N2.
Using (E2) again, we have
1n[|θn(t)−1|‖un‖→p(⋅)+tθn(t)‖φ‖→p(⋅)]≥J(un)−J(θn(t)(un+tφ))=∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx−∫Ωg(x)uq(x)+1nq(x)+1dx+∫Ωf(x)u1−β(x)nβ(x)−1dx−∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)pi(x)dx+∫Ωg(x)[θn(t)(un+tφ)]q(x)+1q(x)+1dx−∫Ωf(x)[θn(t)(un+tφ)]1−β(x)β(x)−1dx=−∫ΩN∑i=1|∂xi(un+tφ)|pi(x)pi(x)dx+∫ΩN∑i=1|∂xiun|pi(x)pi(x)dx−∫Ωf(x)(un+tφ)1−β(x)β(x)−1dx+∫Ωf(x)u1−β(x)nβ(x)−1dx−∫ΩN∑i=1|∂xiθn(t)(un+tφ)|pi(x)pi(x)dx+∫ΩN∑i=1|∂xi(un+tφ)|pi(x)pi(x)dx−∫Ωf(x)[θn(t)(un+tφ)]1−β(x)β(x)−1dx+∫Ωf(x)(un+tφ)1−β(x)β(x)−1dx∫Ωg(x)[θn(t)(un+tφ)]q(x)+1q(x)+1dx−∫Ωg(x)(un+tφ)q(x)+1q(x)+1dx−∫Ωg(x)uq(x)+1nq(x)+1dx+∫Ωg(x)(un+tφ)q(x)+1q(x)+1dx | (3.33) |
Dividing by t and passing to the limit as t→0 gives
1n[|θ′n(0)|‖un‖→p(⋅)+‖φ‖→p(⋅)]≥−∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx+∫Ωf(x)u−β(x)nφdx+∫Ωg(x)uq(x)nφdx[−∫ΩN∑i=1|∂xiun|pi(x)dx+∫Ωg(x)uq(x)+1ndx+∫Ωf(x)u1−β(x)ndx]θ′n(0)=−∫ΩN∑i=1|∂xiun|pi(x)−2∂xiun⋅∂xiφdx+∫Ωg(x)uq(x)nφdx+∫Ωf(x)u−β(x)nφdx | (3.34) |
If we consider that |θ′n(0)|≤C0 uniformly in n, we obtain that ∫Ωf(x)u−β(x)ndx<∞. Therefore, for n→∞ it reads
∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiφdx−∫Ωg(x)(u∗)q(x)φdx−∫Ωf(x)(u∗)−β(x)φdx≥0 | (3.35) |
for all φ∈W1,→p(⋅)0(Ω), φ≥0. Letting φ=u∗ in (3.35) shows clearly that u∗∈N1.
This means, as with the Case Ⅰ, that we have
u∗∈N2 | (3.36) |
By taking into consideration the results (3.21), (3.22), (3.35), and (3.36), we infer that u∗∈N2 and (3.35) holds, in the weak sense, for both cases. Additionally, since u∗≥0 and u∗≠0, by the strong maximum principle for weak solutions, we must have u∗(x)>0almost everywhere inΩ.
Next, we show that u∗∈W1,→p(⋅)0(Ω) is a weak solution to problem (1.1).
For a random function ϕ∈W1,→p(⋅)0(Ω), and ε>0, let φ=(u∗+εϕ)+=max{0,u∗+εϕ}. We split Ω into two sets as follows:
Ω≥={x∈Ω:u∗(x)+εϕ(x)≥0}, | (3.37) |
and
Ω<={x∈Ω:u∗(x)+εϕ(x)<0}. | (3.38) |
If we replace φ with (u∗+εϕ) in (3.35), it follows
0≤∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiφdx−∫Ω[g(x)(u∗)q(x)+f(x)(u∗)−β(x)]φdx=∫Ω≥N∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xi(u∗+εϕ)dx−∫Ω≥[g(x)(u∗)q(x)(u)∗+f(x)(u∗)−β(x)](u∗+εϕ)dx=∫Ω−∫Ω<[N∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xi(u∗+εϕ)−[g(x)(u∗)q(x)+f(x)(u∗)−β(x)](u∗+εϕ)]dx=∫ΩN∑i=1|∂xiu∗|pi(x)dx+ε∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiϕdx−∫Ωf(x)(u∗)1−β(x)dx−ε∫Ωf(x)(u∗)−β(x)ϕdx−∫Ωg(x)(u∗)q(x)+1dx−ε∫Ωg(x)(u∗)q(x)ϕdx−∫Ω<[N∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xi(u∗+εϕ)−[g(x)(u∗)q(x)+f(x)(u∗)−β(x)](u∗+εϕ)]dx | (3.39) |
Since u∗∈N2, we have
0≤ε[∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiϕ−[g(x)(u∗)q(x)+f(x)(u∗)−β(x)]ϕ]dx−ε∫Ω<N∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiϕdx+ε∫Ω<g(x)(u∗)q(x)ϕdx+ε∫Ω<f(x)(u∗)−β(x)ϕdx | (3.40) |
Dividing by ε and passing to the limit as ε→0, and considering that |Ω<|→0 as ε→0 gives
∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiϕdx−∫Ωg(x)(u∗)q(x)ϕdx≥∫Ωf(x)(u∗)−β(x)ϕdx,∀ϕ∈W1,→p(⋅)0(Ω) | (3.41) |
However, since the function ϕ∈W1,→p(⋅)0(Ω) is chosen randomly, it follows that
∫ΩN∑i=1|∂xiu∗|pi(x)−2∂xiu∗⋅∂xiϕdx−∫Ωg(x)(u∗)q(x)ϕdx=∫Ωf(x)(u∗)−β(x)ϕdx | (3.42) |
which concludes that u∗∈W1,→p(⋅)0(Ω) is a weak solution to problem (1.1).
Suppose that
{g(x)=ekcos(|x|),andf(x)=(1−|x|)kβ(x),x∈B1(0)⊂RN,k>0. |
Then equation (1.1) becomes
{−N∑i=1∂xi(|∂xiu|pi(x)−2∂xiu)=(1−|x|)kβ(x)u−β(x)+ekcos(|x|)uq(x) in B1(0),u>0 in B1(0),u=0 on ∂B1(0). | (4.1) |
Theorem 4.1. Assume that the conditions (A1)−(A3) hold. If 1<β+<1+k+1α and α>1/2, then, problem (4.1) has at least one positive W1,→p(⋅)0(B1(0))-solution.
Proof. Function f(x)=(1−|x|)kβ(x)≤(1−|x|)kβ− is clearly non-negative and bounded above within the unit ball B1(0) since |x|<1. Hence, f(x)∈L1(B1(0)).
Now, let's choose ¯u=(1−|x|)α. Since ¯u is also non-negative and bounded within B(0,1), it is in ¯u∈LP++(B(0,1)). Indeed,
N∑i=1∫B1(0)((1−|x|)α)pi(x)dx≤N[∫B1(0)((1−|x|)α)P−−dx+∫B1(0)((1−|x|)α)P++dx]<∞. |
Next, we show that ∂xi¯u∈Lpi(⋅)(B1(0)) for i∈{1,...,N}. Fix i∈{1,...,N}. Then
∂xi(1−|x|)α=α(1−|x|)α−1−xi|x| |
Considering that x∈B1(0), we obtain
∫B1(0)|∂xi(1−|x|)α|pi(x)dx≤αPM∫B1(0)(1−|x|)(α−1)P−−dx |
Therefore,
N∑i=1∫B1(0)|∂xi(1−|x|)α|pi(x)dx≤NαPMN∑i=1∫B(0,1)(1−|x|)(α−1)P−−dx<∞ |
if α>P−−−1P−−. Thus, ∂xi¯u∈Lpi(⋅)(B1(0)) for i∈{1,...,N}, and as a result, ¯u∈W1,→p(⋅)0(B1(0)).
Finally, we show that ∫B(0,1)(1−|x|)k(1−|x|)α(1−β(x))β(x)dx<∞. Then,
∫B1(0)(1−|x|)k(1−|x|)α(1−β(x))β(x)dx≤1β−∫B1(0)(1−|x|)k+α(1−β+)dx<∞. |
Thus, by Theorem 3.8, problem (4.1) has at least one positive W1,→p(⋅)0(B1(0))-solution.
The author declares he has not used Artificial Intelligence (AI) tools in the creation of this article.
This work was supported by Athabasca University Research Incentive Account [140111 RIA].
The author declares there is no conflict of interest.
[1] |
G. U. Yule, Why do we sometimes get nonsense-correlations between time-series, J. Royal Stat. Soc., 89 (1926), 1–63. https://doi.org/10.1017/CBO9781139170116.012 doi: 10.1017/CBO9781139170116.012
![]() |
[2] |
Y. Wen, Y. Xu, Statistical monitoring of economic growth momentum transformation: empirical study of Chinese provinces, AIMS Math., 8 (2023), 24825–24847. https://doi.org/10.3934/math.20231266 doi: 10.3934/math.20231266
![]() |
[3] |
Z. Li, F. Zou, B. Mo, Does mandatory CSR disclosure affect enterprise total factor productivity, Econ. Res., 35 (2022), 4902–4921. https://doi.org/10.1080/1331677X.2021.2019596 doi: 10.1080/1331677X.2021.2019596
![]() |
[4] |
N. Stanojević, K. Zakić, China and deglobalization of the world economy, Natl. Account. Rev., 5 (2023), 67–85. https://doi.org/10.3934/NAR.2023005 doi: 10.3934/NAR.2023005
![]() |
[5] |
Y. Liu, Z. Li, M. Xu, The influential factors of financial cycle spillover: evidence from China, Emerg. Mark. Financ. Tr., 56 (2020), 1336–1350. https://doi.org/10.1080/1540496X.2019.1658076 doi: 10.1080/1540496X.2019.1658076
![]() |
[6] |
Z. Li, J. Zhong, Impact of economic policy uncertainty shocks on China's financial conditions, Financ. Res. Lett., 35 (2020), 101303. https://doi.org/10.1016/j.frl.2019.101303 doi: 10.1016/j.frl.2019.101303
![]() |
[7] |
Z. Li, B. Mo, H. Nie, Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China, Int. Rev. Econ. Financ., 86 (2023), 46–57. https://doi.org/10.1016/j.iref.2023.01.015 doi: 10.1016/j.iref.2023.01.015
![]() |
[8] |
N. T. Giannakopoulos, D. P. Sakas, N. Kanellos, C. Christopoulos, Web analytics and supply chain transportation firms' financial performance, Natl. Account. Rev., 5 (2023), 405–420. https://doi.org/10.3934/NAR.2023023 doi: 10.3934/NAR.2023023
![]() |
[9] |
Z. Li, Z. Huang, H. Dong, The influential factors on outward foreign direct investment: evidence from the "the belt and road", Emerg. Mark. Financ. Tr., 55 (2019), 3211–3226. https://doi.org/10.1080/1540496X.2019.1569512 doi: 10.1080/1540496X.2019.1569512
![]() |
[10] |
M. Hong, J. He, K. Zhang, Z. Guo, Does digital transformation of enterprises help reduce the cost of equity capital, Math. Biosci. Eng., 20 (2023), 6498–6516. https://doi.org/10.3934/mbe.2023280 doi: 10.3934/mbe.2023280
![]() |
[11] |
Z. Li, J. Zhu, J. He, The effects of digital financial inclusion on innovation and entrepreneurship: a network perspective, Electron. Res. Arch., 30 (2022), 4740–4762. https://doi.org/10.3934/era.2022240 doi: 10.3934/era.2022240
![]() |
[12] |
Z. Li, H. Chen, B. Mo, Can digital finance promote urban innovation? Evidence from China, Borsa Istanb. Rev., 23 (2023), 285–296. https://doi.org/10.1016/j.bir.2022.10.006 doi: 10.1016/j.bir.2022.10.006
![]() |
[13] |
Z. Li, C. Yang, Z. Huang, How does the fintech sector react to signals from central bank digital currencies, Financ. Res. Lett., 50 (2022), 103308. https://doi.org/10.1016/j.frl.2022.103308 doi: 10.1016/j.frl.2022.103308
![]() |
[14] |
Y. Liu, L. Chen, H. Luo, Y. Liu, Y. Wen, The impact of intellectual property rights protection on green innovation: a quasi-natural experiment based on the pilot policy of the Chinese intellectual property court, Math. Biosci. Eng., 21 (2024), 2587–2607. https://doi.org/10.3934/mbe.2024114 doi: 10.3934/mbe.2024114
![]() |
[15] |
Y. Wang, J. Liu, X. Yang, M. Shi, R. Ran, The mechanism of green finance's impact on enterprises' sustainable green innovation, Green Financ., 5 (2023), 452–478. https://doi.org/10.3934/GF.2023018 doi: 10.3934/GF.2023018
![]() |
[16] |
J. Duan, T. Liu, X. Yang, H. Yang, Y. Gao, Financial asset allocation and green innovation, Green Financ., 5 (2023), 512–537. https://doi.org/10.3934/GF.2023020 doi: 10.3934/GF.2023020
![]() |
[17] |
Z. Li, Z. Huang, Y. Su, New media environment, environmental regulation and corporate green technology innovation: evidence from China, Energy Economics, 119 (2023), 106545. https://doi.org/10.1016/j.eneco.2023.106545 doi: 10.1016/j.eneco.2023.106545
![]() |
[18] |
S. K. Agyei, A. Bossman, Investor sentiment and the interdependence structure of GIIPS stock market returns: a multiscale approach, Quant. Financ. Econ., 7 (2023), 87–116. https://doi.org/10.3934/QFE.2023005 doi: 10.3934/QFE.2023005
![]() |
[19] |
J. Saleemi, Political-obsessed environment and investor sentiments: pricing liquidity through the microblogging behavioral perspective, Data Sci. Financ. Econ., 3 (2023), 196–207. https://doi.org/10.3934/DSFE.2023012 doi: 10.3934/DSFE.2023012
![]() |
[20] |
T. C. Chiang, Stock returns and inflation expectations: evidence from 20 major countries, Quant. Financ. Econ., 7 (2023), 538–568. https://doi.org/10.3934/QFE.2023027 doi: 10.3934/QFE.2023027
![]() |
[21] |
C. Granger, N. Hyung, Y. Jeon, Spurious regression with stationary series, Appl. Econ., 33 (2001), 899–904. https://doi.org/10.1080/00036840121734 doi: 10.1080/00036840121734
![]() |
[22] |
C. Granger, P. Newbold, Spurious regressions in econometrics, J. Econometrics, 2 (1974), 111–120. https://doi.org/10.1016/0304-4076(74)90034-7 doi: 10.1016/0304-4076(74)90034-7
![]() |
[23] |
T. H. Kim, Y. S. Lee, P. Newbold, Spurious regressions with stationary processes around linear trends, Econ. Lett., 83 (2004), 257–262. https://doi.org/10.1016/j.econlet.2003.10.020 doi: 10.1016/j.econlet.2003.10.020
![]() |
[24] |
D. Ventosa-Santaulária, Spurious regression, J. Probab. Stat., 2009 (2009), 1–27. https://doi.org/10.1155/2009/802975 doi: 10.1155/2009/802975
![]() |
[25] |
H. Liu, The analysis of spurious regressions instationary processes without drifts, J. Quant. Tech. Econ., 27 (2010), 142–154. https://doi.org/10.13653/j.cnki.jqte.2010.11.001 doi: 10.13653/j.cnki.jqte.2010.11.001
![]() |
[26] |
B. T. McCallum, Is the spurious regression problem spurious, Econ. Lett., 107 (2010), 321–323. https://doi.org/10.1016/j.econlet.2010.02.004 doi: 10.1016/j.econlet.2010.02.004
![]() |
[27] |
H. Liu, A study on the properties and correction of HAC method and its application in the spurious regression, J. Quant. Tech. Econ., 32 (2015), 148–161. https://doi.org/10.13653/j.cnki.jqte.2015.11.010 doi: 10.13653/j.cnki.jqte.2015.11.010
![]() |
[28] |
H. Liu, C. Li, Application of bias-correction prewhitening HAC methods in the spurious regression, J. Quant. Tech. Econ., 30 (2013), 109–123. https://doi.org/10.13653/j.cnki.jqte.2013.08.021 doi: 10.13653/j.cnki.jqte.2013.08.021
![]() |
[29] |
C. Y. Choi, L. Hu, M. Ogaki, Robust estimation for structural spurious regressions and a Hausman-type cointegration test, J. Econometrics, 142 (2008), 327–351. https://doi.org/10.1016/j.jeconom.2007.06.003 doi: 10.1016/j.jeconom.2007.06.003
![]() |
[30] |
M. Wu, Fgls method based on finite samples, J. Quant. Tech. Econ., 30 (2013), 148–160. https://doi.org/10.13653/j.cnki.jqte.2013.07.022 doi: 10.13653/j.cnki.jqte.2013.07.022
![]() |
[31] |
S. H. Sørbye, P. G. Nicolau, H. Rue, Finite-sample properties of estimators for first and second order autoregressive processes, Stat. Infer. Stoch. Pro., 25 (2022), 577–598. https://doi.org/10.1007/s11203-021-09262-4 doi: 10.1007/s11203-021-09262-4
![]() |
[32] |
J. H. Kim, Forecasting autoregressive time series with bias-corrected parameter estimators, Int. J. Forecast., 19 (2003), 493–503. https://doi.org/10.1016/S0169-2070(02)00062-6 doi: 10.1016/S0169-2070(02)00062-6
![]() |
[33] | S. Wang, J. Hu, Trend-cycle decomposition and stochastic impact effect of Chinese GDP, Econ. Res. J., 44 (2009), 65–76. |
[34] |
A. E. Noriega, D. Ventosa-Santaulária, Spurious regression and trending variables, Oxford Bull. Econ. Stat., 69 (2007), 439–444. https://doi.org/10.1111/j.1468-0084.2007.00481.x doi: 10.1111/j.1468-0084.2007.00481.x
![]() |
[35] |
L. García-Belmonte, D. Ventosa-Santaulária, Spurious regression and lurking variables, Stat. Probab. Lett., 81 (2011), 2004–2010. https://doi.org/10.1016/j.spl.2011.08.015 doi: 10.1016/j.spl.2011.08.015
![]() |
[36] |
M. Wu, P. You, Solution of spurious regression with trending variables, J. Quant. Tech. Econ., 12 (2016), 113–128. https://doi.org/10.13653/j.cnki.jqte.2016.12.007 doi: 10.13653/j.cnki.jqte.2016.12.007
![]() |
[37] |
C. S. H. Wang, C. M. Hafner, A simple solution of the spurious regressionproblem, Stud. Nonlinear Dyn. Econ., 22 (2018), 1–14. https://doi.org/10.1515/snde-2015-0040 doi: 10.1515/snde-2015-0040
![]() |
[38] |
C. Kao, Spurious regression and residual-based testsfor cointegration in panel data, J. Econometrics, 90 (1999), 1–44. https://doi.org/10.1016/S0304-4076(98)00023-2 doi: 10.1016/S0304-4076(98)00023-2
![]() |
[39] |
A. Onatski, C. Wang, Spurious factor analysis, Econometrica, 89 (2021), 591–614. https://doi.org/10.3982/ECTA16703 doi: 10.3982/ECTA16703
![]() |
[40] |
M. Khumalo, H. Mashele, M. Seitshiro, Quantification of the stock market value at risk by using fiaparch, hygarch and figarch models, Data Sci. Financ. Econ., 3 (2023), 380–400. https://doi.org/10.3934/DSFE.2023022 doi: 10.3934/DSFE.2023022
![]() |
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