The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.
Citation: Muhammad Nauman Akram, Muhammad Amin, Ahmed Elhassanein, Muhammad Aman Ullah. A new modified ridge-type estimator for the beta regression model: simulation and application[J]. AIMS Mathematics, 2022, 7(1): 1035-1057. doi: 10.3934/math.2022062
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The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.
We consider a singular no-sign obstacle problem of the type
{div(xa1∇u)=xa1f(x)χ{u≠0}in B+1,u=0on B1∩{x1=0}, | (1.1) |
where a>1, χD is the characteristic function of D, B1⊂Rn is the unit ball and B+1=B1∩{x1>0}. The equation is considered in the weak form,
∫B+1xa1∇u∇φdx=∫B+1xa1f(x)φχ{u≠0}dx, |
for all φ∈W1,20(B+1). This problem, when the non-negativity assumption u≥0 is imposed, is already studied in [9]. The above no-sign problem, as a general semilinear PDE with non-monotone r.h.s., introduces certain difficulties and to some extent some challenges. To explain this we shall give a very short review of the existing results and methods for similar type of problems (see also the book [6] and Caffarelli's review of the classical obstacle problem [2]). The general methodology of approaching such problems lies in using the so-called ACF-monotonicity formula (see [8]) or alternatively using John Andersson's dichotomy (see [1] or [3]). Although there are still some chances that both these methods will work for our problem above, we shall introduce a third method here which relies on a softer version of a monotonicity formula (which has a wider applicability) in combination with some elaborated analysis. We refer to this as a Weiss-type monotonicity formula, see (2.1) below.
For clarity of exposition we shall introduce some notations and definitions here that are used frequently in the paper. Throughout this paper, Rn will be equipped with the Euclidean inner product x⋅y and the induced norm |x|, Br(x0) will denote the open n-dimensional ball of center x0, radius r with the boundary ∂Br(x0). In addition, Br=Br(0) and ∂Br=∂Br(0). Rn+ stands for half space {x∈Rn:x1>0} as well as B+r=Br∩Rn+. Moreover, in the text we use the n-dimensional Hausdorff measure Hn. For a multi-index μ=(μ1,⋯,μn)∈Zn+, we denote the partial derivative with ∂μu=∂μ1x1⋯∂μnxnu and |μ|1=μ1+⋯+μn.
For a domain Ω⊂Rn+ and 1≤p<∞, we use the notation Lp(Ω) and Wm,p(Ω) as the standard spaces. However, we need some new notation for the weighted spaces
Lp(Ω;xθ1):={u:∫Ωxθ1|u(x)|pdx<∞}, |
where θ∈R. For m∈N, we define the weighted Sobolev space Wm,p(Ω;xθ1) as the closure of C∞(¯Ω) with the following norm,
‖u‖Wm,p(Ω;xθ1):=‖u‖Lp(Ω;xθ1)+‖x1Du‖Lp(Ω;xθ1)+⋯+‖xm1Dmu‖Lp(Ω;xθ1). |
It is noteworthy that for θ=0, we have Lp(Ω;1)=Lp(Ω) but Wm,p(Ω;1)⊋Wm,p(Ω). Generally, the trace operator has no sense for θ>−1, while functions in W1,p(Ω;xθ1) have zero traces on {x1=0} for θ≤−1. (Theorem 6 in [7]).
We consider u∈W1,p(B+1,xθ1) for some θ<−n and n<p to be a weak solution of (1.1). This condition provides the continuity of x(θ+n)/p1u up to the boundary according to Sobolev embedding Theorem 3.1 in [5]. First, we prove the following a priori regularity result.
Proposition 1.1. (Appendix A) Let u∈W1,p(B+1,xθ1) be a solution of (1.1) for some θ<−n, n<p and f∈L∞(B+1). Then for each max{0,1+θ+np}<β<1 there exists C=C(β,n,a) such that for r≤1/2,
supB+r(x0)|xβ−11u|≤Cr2β, |
for all x0∈{x1=0}.
In Appendix A we will prove this proposition. Our main result in this paper concerns the optimal growth rate of solution u of (1.1) at touching free boundary points, which is stated in the following theorem.
Theorem 1.2. Suppose u∈W1,p(B+1,xθ1) is a solution of (1.1) for some θ<−n, n<p and x0∈∂{u=0}∩{x1=0}∩B+1/4. Moreover, if f∈Cα(¯B+1) for some α∈(0,1), then
|u(x)|≤Cx21((|x−x0|+x1x1)(n+a+4)/2+1), |
for a universal constant C=C(a,n,[f]0,α).
Our main tool in proving optimal decay for solutions from the free boundary points is Weiss-monotonicity formula, combined with some elaborated techniques. We define the balanced energy functional
Φx0(r,u)=r−n−2−a∫B+r(x0)(xa1|∇u|2+2xa1f(x0)u)dx−2r−n−3−a∫∂Br(x0)∩Rn+xa1u2dHn−1. | (2.1) |
Considering the scaling ur,x0=ur(x)=u(rx+x0)r2, Φx0(r,u)=Φ0(1,ur). In what follows we prove almost-monotonicity of the energy.
Lemma 2.1 (Almost-Monotonicity Formula). Let u solve (1.1) and be as in Proposition 1.1 and assume that ∇u(x0)=0 for some x0∈{x1=0} and f∈Cα(¯B+1) for some α∈(0,1). Then u satisfies, for r≤r0 such that B+r0(x0)⊆B+1,
ddrΦx0(r,u)≥2r∫∂B1∩Rn+xa1(∂rur)2dHn−1−Crα+β−2, |
where C depends only on ‖f‖Cα(¯B+1) and the constant C(β,n,a) in Proposition 1.1.
Proof. Let ur(x):=u(rx+x0)r2, then
=12ddrΦx0(r,u)=12ddr[∫B+1(xa1|∇ur|2+2xa1f(x0)ur)dx−2∫∂B1∩Rn+xa1u2rdHn−1]=12[∫B+1(2xa1∇ur⋅∇∂rur+2xa1f(x0)∂rur)dx−4∫∂B1∩Rn+xa1ur∂rurdHn−1]=∫B+1div(xa1∂rur∇ur)−∂rurdiv(xa1∇ur)+xa1f(x0)∂rurdx−2∫∂B1∩Rn+xa1ur∂rurdHn−1=∫B+1(f(x0)−f(rx+x0)χ{ur≠0})xa1∂rurdx+∫∂B+1xa1∂rur∇ur⋅νdHn−1−2∫∂B1∩Rn+xa1ur∂rurdHn−1=∫B+1(f(x0)−f(rx+x0)χ{ur≠0})xa1∂rurdx+r∫∂B1∩Rn+xa1(∂rur)2dHn−1=∫B+1(f(x0)−f(rx+x0))χ{ur≠0}xa1∂rurdx+f(x0)∫B+1∩{ur=0}xa1∂rurdx+r∫∂B1∩Rn+xa1(∂rur)2dHn−1. |
Note that the second integral
∫B+1∩{ur=0}xa1∂rurdx=0 |
as |{ur=0∧∇ur≠0}|=0 and ∂rur=0 on {ur=0∧∇ur=0}. Since |∂rur|≤Crβ−2 we infer that
∫B+1(f(x0)−f(rx+x0))χ{ur≠0}xa1∂rurdx≥−Crα+β−2 |
and conclude that
12ddrΦx0(r,u)≥r∫∂B1∩Rn+xa1(∂rur)2dHn−1−Crα+β−2. |
Definition 2.2. Let HP2 stand for the class of all two-homogeneous functions P∈W1,2(B+1;xa−21) satisfying div(xa1∇P)=0 in Rn+ with boundary condition P=0 on x1=0. We also define the operator Π(v,r,x0) to be the projection of vr,x0 onto HP2 with respect to the inner product
⟨vw⟩=∫∂B1∩n+xa1vwdHn−1. |
We will use the following extension of [10,Lemma 4.1].
Lemma 2.3. Assume that div(xa1∇w)=0 in B+1 with boundary condition w=0 on x1=0, and w(0)=|∇w(0)|=0. Then
∫B+1xa1|∇w|2dx−2∫∂B1∩Rn+xa1w2dHn−1≥0, |
and equality implies that w∈HP2, i.e., it is homogeneous of degree two.
Proof. We define an extension of the Almgren frequency,
r↦N(w,r):=r∫B+rxa1|∇w|2dx∫∂B+rxa1w2dHn−1, |
12N′(w,r)N(w,r)=∫∂B+rxa1(∂νw)2dHn−1∫∂B+rxa1w∂νwdHn−1−∫∂B+rxa1w∂νwdHn−1∫∂B+rxa1w2dHn−1≥0. |
Moreover, if N(w,r)=κ for ρ<r<σ, it implies that w is homogeneous of degree κ in Bσ∖Bρ.
Now supposing towards a contradiction that N(w,s)<2 for some s∈(0,1], and defining wr(x):=w(rx)‖w(rx)‖L2(∂B+1,xb1), we infer from N(w,s)<2 that ∇wr is bounded in L2(B+1;xa1) and so ∇wrm⇀∇w0 weakly in L2(B+1;xa1) and wrm→w0 strongly in L2(∂B+1;xa1) as a sequence rm→0. Consequently, w0 satisfies div(xa1∇w0)=0 in B+1, w0(0)=|∇w0(0)|=0 and w0=0 on x1=0 as well as ‖w0‖L2(∂B+1;xa1)=1. Furthermore, for all r∈(0,1) we have
N(w0,r)=limrm→0N(wrm,r)=limrm→0N(w,rrm)=N(w,0+), |
and so w0 must be a homogeneous function of degree κ:=N(w,0+)<2. Note that for every multi-index μ∈{0}×Zn−1+, the higher order partial derivative ζ=∂μw0 satisfies the equation div(xa1∇ζ)=0 in Rn+. From the integrability and homogeneity we infer that ∂μw0≡0 for κ−|μ|1<−n2, otherwise
∫B+1|∂μw0|2dx=(∫10r2(κ−|μ|1)+n−1dr)∫∂B1∩Rn+|∂μw0|2dHn−1 |
can not be bounded. Thus x′↦w0(x1,x′) is a polynomial, and we can write w0(x1,x′)=xκ1p(x′x1). Consider the multi-index μ such that |μ|1=degp, so ∂μw0=xκ−|μ|11∂μp is a solution of div(xa1∇ζ)=0 in Rn+. Therefore, ∂μw0∈W1,2(B+1;xθ1) for −1<θ according to Proposition A.1, which implies that 2(κ−|μ|1)+θ>−1. So, degp<κ+θ+12.
Substituting w0(x)=xκ−11(αx1+ℓ⋅x′) for κ>1 in the equation and comparing with w0(0)=|∇w0(0)|=0 we arrive at the only nonzero possible case being κ+a=2, which contradicts a>1. The case κ<1 leads to degp=0 and w0(x)=αxκ1, which implies κ+a=1 and a contradiction to a>1.
Proposition 3.1. Let f∈Cα(¯B+1) and u be solution of (1.1) satisfying the condition in Proposition 1.1. Then the function
r↦r−n−3−a∫∂B+r(x0)xa1u2(x)dHn−1 |
is bounded on (0,1/8), uniformly in x0∈∂{u=0}∩{x1=0}∩B1/8.
Proof. Let us divide the proof into steps.
Step 1 We claim that there exists a constant C1<∞ such that for all x0∈∂{u=0}∩{x1=0}∩B1/8 and r≤1/8,
f(x0)∫B+1xa1ux0,r(x)dx≥−C1, |
where ux0,r:=u(rx+x0)r2. To prove this we observe that w:=ux0,r satisfies
div(xa1∇w)=xa1fr(x):=xa1f(x0+rx)χ{ux0,r≠0},in B+1. |
Moreover, for ϕ(ρ):=ρ−n−a+1∫∂B+ρxa1w(x)dHn−1 we have
ϕ′(ρ)=∫∂B+1xa1∇w(ρx)⋅xdHn−1=ρ−n−a+1∫B+ρdiv(xa1∇w(x))dx=ρ−n−a+1∫B+ρxa1fr(x)dx. |
If f(x0)≥18α[f]0,α then fr≥0 for r≤1/8. Therefore ϕ is increasing and ϕ(ρ)≥ϕ(0)=0 (recall that w(0)=0). Similarly, if f(x0)≤−18α[f]0,α, we obtain that ϕ(ρ)≤0. Therefore the claim is true for C1=0 in these cases.
In the case |f(x0)|≤18α[f]0,α, then |fr(x)|≤28α[f]0,α and then
|ϕ′(ρ)|≤21−3α[f]0,αρ−n−a+1∫B+ρxa1dx≤21−3α[f]0,αρ. |
So, |ϕ(ρ)|≤2−3α[f]0,αρ2 and
|f(x0)∫B+1xa1ux0,r(x)dx|=|f(x0)∫10ρn+a−1ϕ(ρ)dρ|≤2−6α[f]20,αn+a+2=:C1. |
Step 2 We claim that there exists a constant C2<∞ such that
distL2(∂B1∩Rn+;xa1)(ux0,r,HP2)≤C2, |
for every x0∈∂{u=0}∩{x1=0}∩B1/8, r≤1/8. Suppose towards a contradiction that this is not true, then there exists a sequence um, xm→ˉx and rm→0 such that
Mm=‖umxm,rm−Π(um,rm,xm)‖L2(∂B1∩Rn+;xa1)→∞,m→∞. |
Let um:=umxm,rm and pm=Π(um,rm,xm) and wm=um−pmMm. Then, since um(0)=|∇um(0)|=0 and by the monotonicity formula and the result of previous step, we find that
∫B+1xa1|∇wm|2dx−2∫∂B+1xa1w2mdHn−1=1M2m[Φxm(rm)−2∫B+1f(xm)xa1umdx]+1M2m∫∂B+1xa1(pm∇pm⋅ν−2um∇pm⋅ν−2p2m+4umpm)dHn−1≤1M2m(Φxm(rm)+2C1)≤1M2m(Φxm(12)+2C1)→0,m→∞. | (3.1) |
Passing to a subsequence such that ∇wm⇀∇w in L2(B+1;xa1) as m→∞, the compact embedding on the boundary implies that ‖w‖L2(∂B1∩Rn+;xa1)=1, and
∫B+1xa1|∇w|2dx≤2∫∂B1xa1w2dHn−1 | (3.2) |
and that
∫∂B1wpdHn−1=0,∀p∈HP2. | (3.3) |
Since div(xa1∇wm)=x1Mmf(xm+r⋅)χ{um≠0}, it follows that div(xa1∇w)=0 in B+1. Moreover, we obtain from Lp-theory that wm→w in C1,αloc(B+1) for each α∈(0,1) as m→∞. Consequently w(0)=|∇w(0)|=0. Thus we can apply Lemma 2.3 and obtain from (3.2) that w is homogeneous of degree 2, contradicting (3.3) and ‖w‖L2(∂B1)=1. This proves the claim.
Step 3 We will show that there exists constant C2 such that for all x0∈∂{u=0}∩{x1=0} satisfying
lim infr→0+|B+r(x0)∩{u=0}||B+r|=0, | (3.4) |
we have
Φx0(0+)−∫B+1xa1f(x0)ux0,rdx≥−C2rα|f(x0)|. |
In order to see this, we can observe that
∫B+1xa1f(x0)ux0,r(x)dx=f(x0)∫10∫10∂s[∫∂B+ρ(sx1)aux0,r(sx)dHn−1(x)]dsdρ=f(x0)∫10ρ∫10∫∂B+ρ(sx1)a∇ux0,r(sx)⋅νdHn−1dsdρ=f(x0)∫10ρ∫10s∫B+ρdiv((sx1)a∇ux0,r(sx))dxdsdρ=f(x0)∫10ρ∫10s∫B+ρ(sx1)af(rsx)χΩx0,r(sx)dxdsdρ=f(x0)2∫10ρ1+a+n∫10s1+a∫B+1xa1dxdsdρ+f(x0)∫10ρ1+a+n∫10s1+a∫B+1xa1(f(rsρx)−f(x0))dxdsdρ≤f(x0)2(n+a+2)(a+2)∫B+1xb1dx+C2rα|f(x0)|. |
Now by condition (3.4), consider a sequence rm→0 such that |B+rm(x0)∩{v=0}||B+rm|→0 and assume that ∇(ux0,rm−px0,rm)⇀∇w in L2(B+1;xa1) as m→∞. Observe now div(xa1w)=f(x0) in B+1 and by similar calculation as above we will have
∫B+1xa1f(x0)wdx=f(x0)2(n+a+2)(a+2)∫B+1xa1dx. |
On the other hand,
\begin{align*} \Phi_{x^0}(0+) & = \lim\limits_{m\to\infty}\Phi_{x^0}(r_{m})\\ & \ge\int_{B_{1}^+}(x_1^a\lvert\nabla w\rvert^{2}+2f(x^0)x_1^aw)dx-2\int_{\partial B_{1}^+}x_1^aw^{2}{\mathop{}\!d} {\mathcal H}^{n-1}\\ & = \int_{B_{1}^+}(-w \operatorname{div}(x_1^a \nabla w)+2f(x^0)x_1^aw)dx\\ & = \int_{B_{1}^+}x_1^af(x^0)w\,dx = \frac{f(x^0)^{2}}{(n+a+2)(a+2)} \int_{B_{1}^+}x_1^{a}{\mathop{}\!d} x. \end{align*} |
Step 4 In this step, we prove the proposition for the points satisfying condition (3.4). For these points, we have
\begin{align*} &\frac12\partial_{r}\Big[\int_{\partial B_{1}^+}x_1^au_{x^0,r}^{2}\,d {\mathcal H}^{n-1}\Big] = \int_{\partial B_{1}^+}x_1^au_{x^0,r}\partial_r u_{x^0,r}\,d {\mathcal H}^{n-1} = \frac{1}{r}\int_{\partial B_{1}^+}x_1^au_{x^0,r}(\nabla u_{x^0,r}\cdot x-2u_{x^0,r}){\mathop{}\!d} {\mathcal H}^{n-1}\\ & = \frac{1}{r}\int_{B_{1}^+}(x_1^a\lvert \nabla u_{x^0,r}\rvert^{2}+u_{x^0,r} \operatorname{div}(x_1^a \nabla u_{x^0,r}))dx -\frac{2}{r}\int_{\partial B_{1}^+}x_1^au_{x^0,r}^{2}{\mathop{}\!d} {\mathcal H}^{n-1}\\ & = \frac{1}{r}\left(\Phi_{x^0}(r)-2\int_{B_{1}^+} f(x^0)x_1^au_{x^0,r}(x)\,dx\right. \left.+\int_{B_{1}^+}x_1^af(rx)u_{x^0,r}(x)\,dx\right)\\ & = \frac{1}{r}\left(\Phi_{x^0}(r)-\int_{B_{1}^+}f(x^0)x_1^au_{x^0,r}dx\right) +\frac{1}{r}\left(\int_{B_{1}^+}(f(rx)-f(x^0))x_1^au_{x^0,r}(x)\,dx\right)\\ & \ge\frac{1}{r}\left(\Phi_{x^0}(0^{+})-\int_{B_{1}^+}f(x^0)x_1^au_{x^0,r}dx\right)-Cr^{\alpha+\beta-2}-C_3r^{\alpha+\beta-2} \ge-C_{2}r^{\alpha-1}-Cr^{\alpha+\beta-2}-C_3r^{\alpha+\beta-2} \end{align*} |
Thus r\mapsto\partial_{r}\left[\int_{\partial B_{1}^+}x_1^au_{x^0, r}^{2}{\mathop{}\!d} {\mathcal H}^{n-1}\right] is integrable and we obtain uniform boundedness of \int_{\partial B_{1}^+}x_1^au_{x^0, r}^{2}d {\mathcal H}^{n-1} = r^{-n-3-b}\int_{\partial B_{r}(x^0)^+}x_1^au^{2}{\mathop{}\!d} {\mathcal H}^{n-1} for all points with property (3.4). It follows that the boundedness holds uniformly on the closure of those points x^0 .
Step 5 We now consider the case
\liminf\limits_{r\to0^{+}}\frac{|B_r^+(x^0)\cap\{u = 0\}| }{|B_r^+|} \gt 0. |
Let us assume towards a contradiction that there are sequences u^m , r_{m} and x^m such that and M_{m} = \|{u_{x^m, r_m}}\|_{ {\mathcal L}^{2}(\partial B_{1}^+)}\to+\infty as m\to\infty . Setting w_{m} = \frac{u^m_{x^m, r_{m}}}{M_{m}} we obtain, as in Step 2, that a subsequence of w_{m} converges weakly in W^{1, 2}(B_{1}^+; x_1^{a-2}) to a function w , with \|w_\|{ {\mathcal L}^{2}(\partial B_{1}^+; x_1^a)} = 1 , w(0) = \lvert\nabla w(0)\rvert = 0 , \operatorname{div}(x_1^a\nabla w) = 0 and
\int_{B_{1}^+}x_1^a\lvert\nabla w|^{2}dx\le2\int_{\partial B_{1}^+}x_1^aw^{2}{\mathop{}\!d} {\mathcal H}^{n-1}. |
According to Lemma 2.3, w\in\mathbb{HP}_2 . In addition we now know that
\int_{B_{1}^+}\chi_{\{w = 0\}}\ge\limsup\limits_{r_{m}\to0^{+}}\int_{B_{1}^+}\chi_{\{u_{x^0,r_{m}} = 0\}} \gt 0. |
This however contradicts the analyticity of w inside B_1^+ , knowing that \|w_\|{ {\mathcal L}^{2}(\partial B_{1};x_1^a)} = 1 .
Now we are ready to prove the main result of the article.
Proof of Theorem 1.2. From Theorem 8.17 in [4], we know that if \operatorname{div}(b(x)\nabla w) = g such that 1\leq b(x)\leq 5^a , then there exists a universal constant C = C(a, n) such that
\|w\|_{ {\mathcal L}^\infty(B_{R/2})}\leq C(a,n)\left(R^{-n/2}\|w\|_{ {\mathcal L}^2(B_R)}+R^{2}\|g\|_{ {\mathcal L}^\infty(B_R)}\right). |
Now for x^0 \in \partial\{u = 0\}\cap\{x_1 = 0\}\cap B_{1/8}^+ and an arbitrary point y\in \partial B_r^+(x^0) , we apply the above estimate for R = 2\delta/3 , w = (\delta/3)^au and equation \operatorname{div}\left(b(x)\nabla w\right) = x_1^af\chi_{\{u\neq0\}} , where \delta = y_1 and b(x) = \frac{x_1^a}{(\delta/3)^a} . Note that 1\leq b(x)\leq 5^a in B_{2\delta/3}(y) and
|u(y)|\leq C(a,n)\left((2\delta/3)^{-n/2}\|u\|_{ {\mathcal L}^2(B_{2\delta/3}(y))}+(2\delta/3)^{2}5^a\|f\|_{ {\mathcal L}^\infty(B_{2\delta/3}(y))}\right). |
According to Proposition 3.1,
\begin{align*} \|u\|^2_{ {\mathcal L}^2(B_{2\delta/3}(y))}\leq & \Big(\frac3\delta\Big)^a\int_{B_{2\delta/3}(y)}x_1^a|u|^2\,dx\\ \leq & \Big(\frac3\delta\Big)^a\int_{B_{r+2\delta/3}(x^0)}x_1^a|u|^2\,dx \\ \leq &C\Big(\frac3\delta\Big)^a(r+2\delta/3)^{n+a+4}. \end{align*} |
Hence,
\begin{equation*} |u(y)|\leq C\left(\delta^{-(n+a)/2}(r+\delta)^{(n+a+4)/2}+\delta^{2}\right)\leq Cy_1^2\left(\Big(\frac{r+y_1}{y_1}\Big)^{(n+a+4)/2}+1\right). \end{equation*} |
From this theorem it follows that solutions have quadratic growth inside cones.
Corollary 3.2. Suppose u is a solution of (1.1) satisfying the condition in Proposition 1.1 and x^0\in \partial\{u = 0\}\cap\{x_1 = 0\}\cap B_{1/8}^+ . Then, for every constant \tau > 0 ,
\sup\limits_{B_r(x^0)\cap {\mathcal C}}|u|\leq C\left(\left(\frac{1}{\tau}+ 1\right)^{\frac{n+a+4}{2}}+1\right) r^2, |
where {\mathcal C} : = \{x: x_1\geq \tau |x-x^0|\} .
This paper was prepared while M. Fotouhi was visiting KTH Royal Institute of Technology. A. Minne was supported by the Knut and Alice Wallenberg Foundation. H. Shahgholian was supported by Swedish Research Council.
The authors declare no conflict of interest.
Let u be a solution of (1.1) for f\in {\mathcal L}^\infty(B_1^+) . We are going to show a priori regularity for solutions to (1.1). Consider the operator {\mathcal L}_{a, c}u: = x_1^2\Delta u+ax_1\partial_1u-cu . The following proposition is the regularity result related to this operator which has been proven by Krylov [5,Theorem 2.7,Theorem 2.8].
Proposition A.1. i) For any a\in {\mathbb R}, p > 1 and \theta\in {\mathbb R} there exists a constant c_0 > 0 such that for any c\geq c_0 the operator {\mathcal L}_{a, c} is a bounded one-to-one operator from W^{2, p}({\mathbb R}^n_+; x_1^\theta) onto {\mathcal L}^p({\mathbb R}^n_+; x_1^\theta) and its inverse is also bounded, in particular for any u\in W^{2, p}({\mathbb R}^n_+; x_1^\theta)
\|u\|_{W^{2,p}( {\mathbb R}^n_+;x_1^\theta)}\leq C\| {\mathcal L}_{a,c}u\|_{ {\mathcal L}^p( {\mathbb R}^n_+;x_1^\theta)}, |
where C is independent of u and c .
ii) The statement in i) is valid for the operator {\mathcal L}_{a, 0} when -1 < \theta < a-2 and a > 1 or either a-2 < \theta < -1 and a < 1 .
Now we can deduce a priori regularity result for u as follows.
Proof of Proposition 1.1. Notice that x_1^{\beta-1}u\in C(\overline{B_1^+}) due to Sobolev embedding Theorem 3.1 in [5]. Then if the statement of proposition fails, there exists a sequence u_j of solutions (1.1), x^j\in \{x_1 = 0\} and r_j\rightarrow0 such that
\sup\limits_{B_r^+(x^j)}|x_1^{\beta-1}u_j|\leq jr^{1+\beta/2},\quad \forall r\geq r_j, \qquad \sup\limits_{B_{r_j}^+(x^j)}|x_1^{\beta-1}u_j| = jr_j^{1+\beta/2}. |
In particular, the function \tilde u_j(x) = \frac{u_j(x^j+r_jx)}{jr_j^{1+\beta/2}} , satisfies
\begin{equation} \sup\limits_{B_R^+}|x_1^{\beta-1}\tilde u_j|\leq R^{1+\beta/2}, \qquad\text{for }1\leq R\leq\frac1{r_j}, \end{equation} | (A.1) |
and with equality for R = 1 , along with
\begin{equation} {\mathcal L}_{a,c_0}\tilde u_j = \frac{r_j^{1-\beta/2}}{j}f(x^j+r_jx)-c_0\tilde u_j, \end{equation} | (A.2) |
where c_0 is defined in Proposition A.1. According to (A.1), the right hand side of (A.2) is uniformly bounded in {\mathcal L}^p(B_R^+; x_1^\theta) for p(\beta-1)-1 < \theta\leq-1 . From here and Proposition A.1 we conclude that \{\tilde u_j\} is bounded in W^{2, p}(B_{R}^+; x_1^\theta) for some \theta\leq-1 and there is a convergent subsequence, tending to a function u_0 with properties
\begin{equation} \sup\limits_{B_R^+}|x_1^{\beta-1} u_0|\leq R^{1+\beta/2},\quad\forall R\geq1,\quad\sup\limits_{B_1^+}|x_1^{\beta-1} u_0| = 1,\quad \operatorname{div}(x_1^a \nabla u_0) = 0, \end{equation} | (A.3) |
as well as the condition \theta\leq-1 insures that the trace operator is well defined and u_0 is zero on \{x_1 = 0\} . The Liouville type theorem in [9,Lemma 20]) implies that
|D^2u_0(x^0)|\leq \frac C{R^2}\sup\limits_{B_R^+(x^0)}|u_0|\leq \frac{C(R+|x_0|)^{2-\beta/2}}{R^2}\rightarrow0. |
Therefore, u_0 is a linear function, which contradicts (A.3).
[1] |
M. N. Akram, M. Amin, M. Amanullah, Two-parameter estimator for the inverse Gaussian regression model, Commun. Stat. Simul. C., 2020. doi: 10.1080/03610918.2020.1797797. doi: 10.1080/03610918.2020.1797797
![]() |
[2] | A. E. Hoerl, R. W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12 (1970), 55–67. |
[3] |
A. F. Lukman, K. Ayinde, S. Binuomote, O. A. Clement, Modified ridge‐type estimator to combat multicollinearity: Application to chemical data, J. Chemometr., 33 (2019), e3125. doi: 10.1002/cem.3125. doi: 10.1002/cem.3125
![]() |
[4] |
A. F. Lukman, A. Emmanuel, O. A. Clement, K. Ayinde, A Modified Ridge-Type Logistic Estimator, Iran. J. Sci. Technol. Trans. Sci., 44 (2020), 437–443. doi: 10.1007/s40995-020-00845-z. doi: 10.1007/s40995-020-00845-z
![]() |
[5] |
A. J. Lemonte, S. L. P. Ferrari, F. Cribari-Neto, Improved likelihood inference in Birnbaum-saunders regressions, Comput. Stat. Data An., 54 (2010), 1307–131. doi: 10.1016/j.csda.2009.11.017. doi: 10.1016/j.csda.2009.11.017
![]() |
[6] |
B. F. Swindel. Good ridge estimators based on prior information, Commun. Stat. Theor. M., 5 (1976), 1065–1075. doi: 10.1080/03610927608827423. doi: 10.1080/03610927608827423
![]() |
[7] |
B. Singh, Y. P. Chaubey, On some improved ridge estimators, Statistische Hefte, 28 (1987), 53–67. doi: 10.1007/BF02932590. doi: 10.1007/BF02932590
![]() |
[8] |
B. Segerstedt, On ordinary ridge regression in generalized linear models, Commun. Stat. Theor. M., 21 (1992), 2227–2246. doi: 10.1080/03610929208830909. doi: 10.1080/03610929208830909
![]() |
[9] |
B. M. G. Kibria, Performance of some new ridge regression estimators, Commun. Stat. Simul. C., 32 (2003), 419–435. doi: 10.1081/SAC-120017499. doi: 10.1081/SAC-120017499
![]() |
[10] |
B. M. G. Kibria, Some Liu and ridge-type estimators and their properties under the ill-conditioned Gaussian linear regression model, J. Stat. Comput. Sim., 82 (2012), 1–17. doi: 10.1080/00949655.2010.519705. doi: 10.1080/00949655.2010.519705
![]() |
[11] | C. Stein, Inadmissibility of the usual estimator for the mean of a multivariate normal distribution, In: Volume 1 contribution to the theory of statistics, Berkeley: University of California Press, 1956,197–206. doi: 10.1525/9780520313880-018. |
[12] |
E. Vigneau, M. F. Devaux, E. M. Qannari, P. Robert, Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration, J. Chemometr., 11 (1998), 239–249. doi: 10.1002/(SICI)1099-128X(199705)11:3<239::AID-CEM470>3.0.CO;2-A. doi: 10.1002/(SICI)1099-128X(199705)11:3<239::AID-CEM470>3.0.CO;2-A
![]() |
[13] | R. W. Farebrother, Further results on the mean square error of ridge regression, J. R. Stat. Soc. B, 38 (1976), 248–250. |
[14] |
G. Trenkler, H. Toutenburg, Mean squared error matrix comparisons between biased estimators-An overview of recent results, Stat. Pap., 31 (1990), 165. doi: 10.1007/BF02924687. doi: 10.1007/BF02924687
![]() |
[15] | G. C. McDonald, D. I. Galarneau, A Monte Carlo evaluation of some ridge-type estimators, J. Am. Stat. Assoc., 70 (1975), 407–416. |
[16] |
K. Månsson, G. Shukur, On ridge parameters in logistic regression, Commun. Stat. Theor. M., 40 (2011), 3366–3381. doi: 10.1080/03610926.2010.500111. doi: 10.1080/03610926.2010.500111
![]() |
[17] |
K. Månsson, G. Shukur, A Poisson ridge regression estimator, Econ. Model., 28 (2011), 1475–1481. doi: 10.1016/j.econmod.2011.02.030. doi: 10.1016/j.econmod.2011.02.030
![]() |
[18] | K. Månsson, B. M. G. Kibria, P. Sjölander, G. Shukur, V. Sweden, New Liu estimators for the poisson regression model: Model and application, HUI Research, 2011. |
[19] |
K. Månsson, B. M. G. Kibria, G. Shukur, On Liu estimators for the logit regression model, Econ. Model., 29 (2012), 1483–1488. doi: 10.1016/j.econmod.2011.11.015. doi: 10.1016/j.econmod.2011.11.015
![]() |
[20] |
K. Månsson, Developing a Liu estimator for the negative binomial regression model: Method and application, J. Stat. Comput. Sim., 83 (2013), 1773–1780. doi: 10.1080/00949655.2012.673127. doi: 10.1080/00949655.2012.673127
![]() |
[21] | L. Kejian, A new class of biased estimate in linear regression, Commun. Stat. Theor. M., 22 (1993), 393–402. doi:.1080/03610929308831027. |
[22] |
L. Kejian, Using Liu-type estimator to combat collinearity, Commun. Stat. Theor. M., 32 (2003), 1009–1020. doi: 10.1081/STA-120019959. doi: 10.1081/STA-120019959
![]() |
[23] |
L. S. Mayer, T. A. Willke, On biased estimation in linear models, Technometrics, 15 (1973), 497–508. doi: 10.1080/00401706.1973.10489076. doi: 10.1080/00401706.1973.10489076
![]() |
[24] | A. F. Lukman, K. Ayinde, Review and classifications of the ridge parameter estimation techniques, Hacet. J. Math. Stat., 46 (2017), 953–967. |
[25] |
M. I. Alheety, B. M. G. Kibria, Modified Liu-type estimator based on (r-k) class estimator, Commun. Stat. Theor. M., 42 (2013), 304–319. doi: 10.1080/03610926.2011.577552. doi: 10.1080/03610926.2011.577552
![]() |
[26] |
M. Amini, M. Roozbeh, Optimal partial ridge estimation in restricted semiparametric regression models, J. Multivariate Anal., 136 (2015), 26–40. doi: 10.1016/j.jmva.2015.01.005. doi: 10.1016/j.jmva.2015.01.005
![]() |
[27] |
M. Arashi, S. M. M. Tabatabaey, B. H. Bashtian, Shrinkage ridge estimators in linear regression, Commun. Stat. Simul. C., 43 (2014), 871–904. doi: 10.1080/03610918.2012.718838. doi: 10.1080/03610918.2012.718838
![]() |
[28] |
M. Qasim, K. Månsson, M. Amin, B. M. G. Kibria, P. Sjolander, Biased adjusted Poisson ridge estimators-method and application, Iran. J. Sci. Technol. Tran. Sci., 44 (2020), 1775–1789. doi: 10.1007/s40995-020-00974-5. doi: 10.1007/s40995-020-00974-5
![]() |
[29] |
M. Amin, M. N. Akram, M. Amanullah, On the James-Stein estimator for the poisson regression model, Commun. Stat. Simul. C., 2020, 1–13. doi: 10.1080/03610918.2020.1775851. doi: 10.1080/03610918.2020.1775851
![]() |
[30] |
M. Amin, M. Qasim, M. Amanullah, S. Afzal, Performance of some ridge estimators for the gamma regression model, Stat. Pap., 61 (2020), 997–1026. doi: 10.1007/s00362-017-0971-z. doi: 10.1007/s00362-017-0971-z
![]() |
[31] |
M. Qasim, K. Månsson, B. M. G. Kibria, On some beta ridge regression estimators: method, simulation and application, J. Stat. Comput. Sim., 91 (2021), 1699–1712. doi: 10.1080/00949655.2020.1867549. doi: 10.1080/00949655.2020.1867549
![]() |
[32] | M. I. Alheety, B. M. G. Kibria, On the Liu and almost unbiased Liu estimators in the presence of multicollinearity with heteroscedastic or correlated errors, Surv. Math. Appl., 4 (2009), 155–167. |
[33] |
M. N. Akram, M. Amin, M. Qasim, A new Liu-type estimator for the inverse Gaussian regression model, J. Stat. Comput. Sim., 90 (2020), 1153–1172. doi: 10.1080/00949655.2020.1718150. doi: 10.1080/00949655.2020.1718150
![]() |
[34] |
M. Qasim, M. Amin, M. Amanullah, On the performance of some new Liu parameters for the gamma regression model, J. Stat. Comput. Sim., 88 (2018), 3065–3080. doi: 10.1080/00949655.2018.1498502. doi: 10.1080/00949655.2018.1498502
![]() |
[35] |
M. Qasim, B. M. G. Kibria, K. Månsson, P. Sjölander, A new Poisson Liu regression estimator: method and application, J. Appl. Stat., 47 (2020), 2258–2271. doi: 10.1080/02664763.2019.1707485. doi: 10.1080/02664763.2019.1707485
![]() |
[36] |
M. Amin, M. A. Ullah, G. M. Cordeiro, Influence diagnostics in the gamma regression model with adjusted deviance residuals, Commun. Stat. Simul. C., 46 (2017), 6959–6973. doi: 10.1080/03610918.2016.1222420. doi: 10.1080/03610918.2016.1222420
![]() |
[37] |
M. Amin, M. A. Ullah, M. Aslam, Empirical evaluation of the inverse Gaussian regression residuals for the assessment of influential points, J. Chemometr., 30 (2016), 394–404. doi: 10.1002/cem.2805. doi: 10.1002/cem.2805
![]() |
[38] |
M. Amin, M. Faisal, M. N. Akram, Influence diagnostics in the inverse gaussian ridge regression model: Applications in chemometrics, J. Chemometr., 35 (2021), e3342. doi: 10.1002/cem.3342. doi: 10.1002/cem.3342
![]() |
[39] |
M. Amin, M. Qasim, S. Afzal, K. Naveed, New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data, Commun. Stat. Simul. C., 2020. doi: 10.1080/03610918.2020.1797794. doi: 10.1080/03610918.2020.1797794
![]() |
[40] |
M. Meloun, J. Militký, Detection of single influential points in OLS regression model building, Anal. Chim. Acta, 439 (2001), 169–191. doi: 10.1016/S0003-2670(01)01040-6. doi: 10.1016/S0003-2670(01)01040-6
![]() |
[41] |
G. Muniz, B. M. G. Kibria, On some ridge regression estimators: An empirical comparisons, Commun. Stat. Simul. Comput., 38 (2009), 621–630. doi: 10.1080/03610910802592838. doi: 10.1080/03610910802592838
![]() |
[42] |
M. Roozbeh, M. Arashi, Feasible ridge estimator in partially linear models, J. Multivariate Anal., 116 (2013), 35–44. doi: 10.1016/j.jmva.2012.11.006. doi: 10.1016/j.jmva.2012.11.006
![]() |
[43] |
M. Roozbeh, G. Hesamian, M. G. Akbari, Ridge estimation in semi-parametric regression models under the stochastic restriction and correlated elliptically contoured errors, J. Comput. Appl. Math., 378 (2020), 112940. doi: 10.1016/j.cam.2020.112940. doi: 10.1016/j.cam.2020.112940
![]() |
[44] |
M. Roozbeh, Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion, Comput. Stat. Data Anal., 117 (2018), 45–61. doi: 10.1016/j.csda.2017.08.002. doi: 10.1016/j.csda.2017.08.002
![]() |
[45] |
M. Roozbeh, M. Arashi, N. A. Hamzah, Generalized cross-validation for simultaneous optimization of tuning parameters in ridge regression, Iran. J. Sci. Technol. Trans. Sci., 44 (2020), 473–485. doi: 10.1007/s40995-020-00851-1. doi: 10.1007/s40995-020-00851-1
![]() |
[46] | N. H. Prater, Estimate gasoline yields from crudes, Petrol. Refiner, 35 (1956), 236–238. |
[47] |
P. Karlsson, K. Månsson, B. M. G. Kibria, A Liu estimator for the beta regression model and its application to chemical data, J. Chemometr., 34 (2020), e3300. doi: 10.1002/cem.3300. doi: 10.1002/cem.3300
![]() |
[48] | R. Frisch, Statistical confluence analysis by means of complete regression systems, Universitetets Økonomiske Institute, 1934. |
[49] |
S. Ferrari, F. Cribari-Neto, Beta regression for modeling rates and proportions, J. Appl. Stat., 31 (2004), 799–815. doi: 10.1080/0266476042000214501. doi: 10.1080/0266476042000214501
![]() |
[50] |
A. B. Simas, W. Barreto-Souza, A. V. Rocha, Improved estimators for a general class of beta regression models, Comput. Stat. Data Anal., 54 (2010), 348–366. doi: 10.1016/j.csda.2009.08.017. doi: 10.1016/j.csda.2009.08.017
![]() |
[51] |
Y. Li, H. Yang, A new Liu-type estimator in linear regression model, Stat. Pap., 53 (2012), 427–437. doi: 10.1007/s00362-010-0349-y. doi: 10.1007/s00362-010-0349-y
![]() |
[52] |
Y. Li, H. Yang, A new stochastic mixed ridge estimator in linear regression model, Stat. Pap., 51 (2010), 315–323. doi: 10.1007/S00362-008-0169-5. doi: 10.1007/S00362-008-0169-5
![]() |
[53] |
Z. Y. Algamal, M. H. Lee, A. M. Al-Fakih, M. Aziz, High-dimensional QSAR prediction of anticancer potency of imidazo[4, 5-b]pyridine derivatives using adjusted adaptive LASSO, J. Chemometr., 29 (2015), 547–556. doi: 10.1002/cem.2741. doi: 10.1002/cem.2741
![]() |
[54] |
Z. Y. Algamal, M. K. Qasim, M. H. Lee, T. H. M. Ali, High-dimensional QSAR/QSPR classification modeling based on improving pigeon optimization algorithm, Chemometr. Intell. Lab., 206 (2020), 104170. doi: 10.1016/j.chemolab.2020.104170. doi: 10.1016/j.chemolab.2020.104170
![]() |
[55] |
Z. Y. Algamal, M. K. Qasim, M. H. Lee, T. H. M. Ali, Improving grasshopper optimization algorithm for hyper-parameters estimation and feature selection in support vector regression, Chemometr. Intell. Lab., 208 (2020), 104196. doi: 10.1016/j.chemolab.2020.104196. doi: 10.1016/j.chemolab.2020.104196
![]() |
[56] | D. C. Montgomery, G. C. Runger, Applied statistics and probability for engineers, John Wiley & Sons, 2014. |