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Bayesian empirical likelihood of quantile regression with missing observations

Abstract

In this paper, we focus on partially linear varying coefficient quantile regression with observations missing at random, which allows the responses or responses and covariates simultaneously missing. By means of empirical likelihood method, we construct posterior distributions of the parameter in the model, and investigate their large sample properties under fixed prior. Meanwhile, we use a Bayesian hierarchical model based on empirical likelihood, spike and slab Gaussian priors to discuss variable selection. By using MCMC algorithm, finite sample performance of the proposed methods is investigated via simulations, and real data analysis is discussed too.

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Acknowledgements

The authors are grateful to the editor, associate editor, and anonymous referees for their helpful comments. This research was supported by the National Natural Science Foundation of China (12071348).

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Correspondence to Han-Ying Liang.

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Appendix

Appendix

Proof of Lemma 7.2

(1) Let \(R_i=I(Y_i-X_i^T\beta -Z_i^T\widetilde{\alpha }(U_i)\le 0)-I(\epsilon _i\le 0)\) and write

$$\begin{aligned}&\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\widetilde{g}^T_i(\beta )\\&\quad =\frac{1}{n}\sum _{i=1}^n \frac{\delta _iX_iX_i^T}{\pi _i^2(\gamma _0)}[I(\epsilon _i\le 0)-\tau ]^2 \\&\qquad +\frac{2}{n}\sum _{i=1}^n \frac{\delta _iX_iX_i^T}{\pi _i^2(\gamma _0)}[I(\epsilon _i\le 0)-\tau ]R_i +\frac{1}{n}\sum _{i=1}^n \frac{\delta _iX_iX_i^T}{\pi _i^2(\gamma _0)}R_i^2\\&\qquad +\frac{1}{n}\sum _{i=1}^n \frac{\delta _iX_iX_i^T[\pi _i^2(\gamma _0)-\pi _i^2(\widehat{\gamma })]}{\pi _i^2(\widehat{\gamma })\pi _i^2(\gamma _0)} [I(Y_i-X_i^T\beta -\widetilde{\alpha }(U_i,\beta )\le 0)-\tau ]^2\\&\quad :=B_{1n}+B_{2n}+B_{3n}+B_{4n}. \end{aligned}$$

Clearly, \(B_{1n}{\mathop {\rightarrow }\limits ^{P}}E(\frac{\delta XX^T}{\pi ^2(\gamma _0)}[I(\epsilon \le 0)-\tau ]^2)=\tau (1-\tau )E[\pi ^{-1}(\gamma _0)XX^T]=V\). Then, it suffices to show that \(B_{kn}\overset{p}{\rightarrow }0\) for \(k=2,3,4\).

By (A5) and Lemma 7.1(1), \(B_{4n}=O_p(n^{-1/2})\). From \(E(\Vert B_{2n}\Vert )\le CE(|R_i|)\) and \(E(\Vert B_{3n}\Vert )\le CE(|R_i|)\), we only need to prove \(E(|R_i|)\rightarrow 0\). In fact,

$$\begin{aligned} |R_i|\le&I(-|X_i^T(\beta -\beta _{\tau })|-|Z_i^T[\widetilde{\alpha }(U_i)-\alpha _\tau (U_i)]|\\&\le \epsilon _i\le |X_i^T(\beta -\beta _{\tau })|+|Z_i^T[\widetilde{\alpha }(U_i)-\alpha _\tau (U_i)]|). \end{aligned}$$

From Lemma (7.1)(2) and \(E\{\frac{1}{nh_n}\sum _{j=1}^n \frac{\delta _jZ_j}{\pi _{j}(\gamma _0)}K(\frac{U_j-U_i}{h_n})[\tau -I(\epsilon _j\le 0)]\}^2=O(\frac{1}{nh_n})\), we have \(\widetilde{\alpha }(U_i)-\alpha _\tau (U_i)=O_p((nh_n)^{-1/2})\). Thus

$$\begin{aligned} E|R_i|&\,\le P(-|X_i^T(\beta -\beta _\tau )|-|Z_i^T(\widetilde{\alpha }(U_i)-\alpha _\tau (U_i))| \le \epsilon _i \le |X_i^T(\beta -\beta _\tau )|\\&\,+|Z_i^T(\widetilde{\alpha }(U_i)-\alpha _\tau (U_i))|)\\&\,\le P(-c_1(\rho _n+(nh_n)^{-1/2})\le \epsilon _i\le c_2(\rho _n+(nh_n)^{-1/2})) \\&+P(\Vert \widetilde{\alpha }(U_i)-\alpha _\tau (U_i)\Vert \\&\,\ge C_0(nh_n)^{-1/2})\\&\,=o(1). \end{aligned}$$

(2) Let \(g^*=\max _{1\le i\le n}\sup _{\Vert \beta -\beta _{\tau }\Vert \le c\rho _n}\Vert \widetilde{g}_i(\beta )\Vert \) and \(\alpha \in \mathbb {S}^p\) such that \(\lambda (\beta )=\Vert \lambda (\beta )\Vert \alpha \). By (2.2) and the fact \(\frac{1}{1+x}=1-\frac{x}{1+x}\), \(\left\{ \frac{1}{n}\sum _{i=1}^n\frac{\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )}{1+\lambda ^T(\beta ) \widetilde{g}_i(\beta )}\right\} \lambda (\beta ) =\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\). Since \(1+\lambda ^T(\beta )\widetilde{g}_i(\beta )>0\), we have

$$\begin{aligned} \Vert \lambda (\beta )\Vert \alpha ^T\left\{ \frac{1}{n} \sum _{i=1}^n\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )\right\} \alpha \le&\Vert \lambda (\beta )\Vert \alpha ^T\left\{ \frac{1}{n}\sum _{i=1}^n \frac{\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )}{1+\lambda ^T(\beta ) \widetilde{g}_i(\beta )}\right\} \alpha (1+\Vert \lambda (\beta )\Vert g^*)\\ =&\alpha ^T\left\{ \frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\right\} (1+\Vert \lambda (\beta )\Vert g^*). \end{aligned}$$

Thus, \(\Vert \lambda (\beta )\Vert \left\{ \alpha ^T\frac{1}{n} \sum _{i=1}^n\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )\alpha -\alpha ^T\frac{g^*}{n} \sum _{i=1}^n\widetilde{g}_i(\beta )\right\} \le \alpha ^T\left\{ \frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\right\} .\) From (7.1), Lemma 7.1(3), \(\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )=O_p(1)\) and \(\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )=O_p(\rho _n+n^{-1/2})\). Clearly, \(g^*=O_p(1)\). Hence, \(\Vert \lambda (\beta )\Vert =O_p(\rho _n+n^{-1/2})\). In particular, \(\Vert \lambda (\beta _\tau )\Vert =O_p(\rho _n+n^{-1/2})\) by taking \(\rho _n=0\). Next, write

$$\begin{aligned} 0=&\frac{1}{n}\sum _{i=1}^n\frac{\widetilde{g}_i(\beta )}{1+\lambda ^T(\beta ) \widetilde{g}_i(\beta )} =\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\left\{ 1-\lambda ^T(\beta )\widetilde{g}_i(\beta ) +\frac{[\lambda ^T(\beta )\widetilde{g}_i(\beta )]^2}{1+\lambda ^T(\beta ) \widetilde{g}_i(\beta )}\right\} \nonumber \\ =&\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )- \left\{ \frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )\widetilde{g}_i^T(\beta )\right\} \lambda (\beta ) +\lambda ^T(\beta )\left\{ \frac{1}{n}\sum _{j=1}^n\frac{[\widetilde{g}_i(\beta )\widetilde{g}^T_i(\beta )] \widetilde{g}_i(\beta )}{1+\lambda ^T(\beta )\widetilde{g}_i(\beta )}\right\} \lambda (\beta ). \end{aligned}$$
(8.1)

Observe that \(\Vert \lambda ^T(\beta )\{\frac{1}{n}\sum _{j=1}^n\frac{[\widetilde{g}_i(\beta ) \widetilde{g}^T_i(\beta )]\widetilde{g}_i(\beta )}{1+\lambda ^T(\beta )\widetilde{g}_i(\beta )}\} \lambda (\beta )\Vert =O_p(\rho _n^2+n^{-1})\), from (8.1) we can get \(\lambda (\beta )=\{\frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta ) \widetilde{g}^T_i(\beta )\}^{-1} \frac{1}{n}\sum _{i=1}^n\widetilde{g}_i(\beta )+O_p(\rho _n^2+n^{-1})\). \(\square \)

Proof of Lemma 7.3

 Set \(h_n(\beta )=\frac{1}{n}\sum _{j=1}^n\widetilde{g}_j(\beta )\) and \(L_n(\beta )=-\sum _{i=1}^n\log (1+n^{\gamma -1} h_n(\beta )^T\widetilde{g}_i(\beta ))\). (2.2) implies \(\ell _n(\beta )\le L_n(\beta )\). Lemmas 7.17.2 implies \(\sum _{i=1}^n\lambda ^T(\beta _{\tau })\widetilde{g}_i(\beta _{\tau })=O_p(1)\). From the fact \(\log (1+x)\le x\) for \(x>-1\), we have \(\ell _n(\beta _{\tau })\ge -\sum _{i=1}^n\lambda ^T(\beta _{\tau })\widetilde{g}_i(\beta _{\tau })=O_p(1)\). Hence,

$$\begin{aligned} P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}\ell _n(\beta )-\ell _n(\beta _{\tau })\le -n^{\gamma }C_{\delta }\right\} \ge P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}L_n(\beta )\le -2n^{\gamma }C_{\delta }\right\} \end{aligned}$$

holds with probability one. Thus, it suffices to show that \(P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}L_n(\beta )\le -2n^{\gamma }C_{\delta }\right\} \rightarrow 1\).

Note that \(-\log (1+n^{\gamma -1} h_n(\beta )^T\widetilde{g}_i(\beta ))\!=\!-n^{\gamma -1}h_n(\beta )^T \widetilde{g}_i(\beta )\!+\! \frac{n^{2(\gamma -1)}[h_n(\beta )^T\widetilde{g}_i(\beta )]^2}{2[1+\alpha n^{\gamma -1}h_n(\beta )^T\widetilde{g}_i(\beta )]^2}\) for some \(\alpha \in (0,1)\). From the boundedness of \(\widetilde{g}_i(\beta )\) and \(h_n(\beta )\) in probability, it follows that

$$\begin{aligned} n^{-\gamma }L_n(\beta )=-n^{-\gamma }\sum _{i=1}^n\log (1+n^{\gamma -1} h_n(\beta )^T\widetilde{g}_i(\beta ))=-\Vert h_n(\beta )\Vert ^2+O_p(n^{\gamma -1}). \end{aligned}$$

Clearly, it only needs to show that \(P\{\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert h_n(\beta )\Vert \ge 2\sqrt{C_{\delta }}\}\rightarrow 1\). In fact, observe that

$$\begin{aligned} h_n(\beta )=&\frac{1}{n}\sum _{i=1}^n\frac{\delta _iX_i}{\pi _i(\gamma _0)} [I(Y_i-X_i^T\beta -Z_i^T\alpha _{\tau }(U_i)\le 0)-\tau ]\\&+\frac{1}{n}\sum _{i=1}^n\frac{\delta _iX_i}{\pi _i(\gamma _0)} [I(Y_i-X_i^T\beta -Z_i^T\widetilde{\alpha }(U_i)\le 0)\\&-I(Y_i-X_i^T\beta -Z_i^T\alpha _{\tau }(U_i)\le 0))]\\&+\frac{1}{n}\sum _{i=1}^n\frac{\delta _iX_i( \pi _i(\gamma _0)-\pi _i(\widehat{\gamma }))}{\pi _i(\widehat{\gamma })\pi _i(\gamma _0)} [I(Y_i-X_i^T\beta -Z_i^T\widetilde{\alpha }(U_i)\le 0)-\tau ]\\&:=A_{1n}+A_{2n}+A_{3n}. \end{aligned}$$

It suffices to verify that \(P\{\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}\Vert \ge 3\sqrt{C_{\delta }}\}\rightarrow 1\) and \(\sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{kn}\Vert \overset{p}{\rightarrow }0\) for \(k=2,3\). From Lemma 7.1(1), \(\sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{3n}\Vert =O_p(n^{-1/2})\).

Using the Bernstein’s inequality, from (A0), (A3) and (A7), it follows that

$$\begin{aligned} \max _{1\le i\le n}\left\| \frac{1}{nh_n}\sum _{j=1}^n\frac{\delta _j[\tau -I(\epsilon _j\le 0)]}{\pi _j(\gamma _0)} K\left( \frac{U_j-U_i}{h_n}\right) \left( \begin{array}{c} X_i\\ Z_i\end{array}\right) \right\| \!=\!o_p(a_n)\,\text{ with }\,a_n=\frac{\log n}{\sqrt{nh_n}}. \end{aligned}$$

Since S(u) is positive define, from Lemma 7.1(2), we have \(\max _{1\le i\le n}\Vert \widetilde{\alpha }(U_i)-\alpha _{\tau }(U_i)\Vert =o_p(a_n)\).

Next, we prove \(\sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{2n}\Vert \overset{p}{\rightarrow }0\). In fact, for each \(\eta >0\), we have

$$\begin{aligned}&P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{2n}\Vert>\eta \right\} \nonumber \\&\qquad \le P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{2n}\Vert>\eta , \max _{1\le i\le n}\Vert \widetilde{\alpha }(U_i)-\alpha _\tau (U_i)\Vert \le Ca_n\right\} \nonumber \\&\qquad \quad +P\left\{ \max _{1\le i\le n}\Vert \widetilde{\alpha }(U_i)-\alpha _\tau (U_i)\Vert>Ca_n\right\} \nonumber \\&\qquad \le P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c} \frac{1}{n}\sum _{i=1}^nI(X_i^T(\beta -\beta _{\tau })-Ca_n\le \epsilon _i\le X_i^T(\beta -\beta _{\tau }) +Ca_n)>C_1\eta \right\} \nonumber \\&\qquad \quad +o(1) :=P\left\{ \sup _{\beta \in \mathcal {B}_\delta ^c}\frac{1}{n}\sum _{i=1}^nf_i(\beta )> \frac{C_1\eta }{2}\right\} +o(1). \end{aligned}$$
(8.2)

It suffices to show \(J_n:=\sup _{\beta \in \mathcal {B}_\delta ^c}\frac{1}{n}\sum _{i=1}^nf_i(\beta )=o_p(1)\). Compact set \(\mathcal {B}_\delta ^c\) can be covered by cubes \(E_1,\ldots ,E_\kappa \) with sides of length at most \(\eta \) and centers \(\beta ^{(1)},\ldots ,\beta ^{(\kappa )}\), respectively, such that \(\kappa \le C(1/\eta )^p\). Hence

$$\begin{aligned} J_{n} \le&\max _{1\le j\le \kappa }\Big |\frac{1}{n}\sum _{i=1}^n [I(-Ca_n-C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }) \le \epsilon _i\le Ca_n+C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }))\\&\quad \quad \quad \quad -EI(-Ca_n-C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }) \le \epsilon _i\le Ca_n+C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }))]\Big |\\&\,+\max _{1\le j\le \kappa }\frac{1}{n}\sum _{i=1}^nE I(-Ca_n-C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }) \le \epsilon _i\le Ca_n+C_1\eta +X_i^T(\beta ^{(j)}-\beta _{\tau }))]\\&:\,=J_{1n}+J_{2n}. \end{aligned}$$

Since \(\kappa \) is finite, law of large number implies \(J_{1n}=O_p(1/\sqrt{n})\). From (A1) we have \(J_{2n}\le C(a_n+\eta )\). Therefore \(J_n=o_p(1)\) and \(\sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{2n}\Vert \overset{p}{\rightarrow }0\) from (8.2).

Finally, we verify \(P\{\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}\Vert \ge 3\sqrt{C_{\delta }}\}\rightarrow 1\). Write \(A_{1n}=A_{1n}'+A_{1n}''\), where

$$\begin{aligned} A_{1n}'=&\frac{1}{n}\sum _{i=1}^n\frac{\delta _iX_i}{\pi _i(\gamma _0)} \{I(Y_i-X_i^T\beta -Z_i^T\alpha _\tau (U_i)\le 0)\\&-EI(Y_i-X_i^T\beta -Z_i^T\alpha _\tau (U_i)\le 0)\},\\ A_{1n}''=&\frac{1}{n}\sum _{i=1}^n\frac{\delta _iX_i}{\pi _i(\gamma _0)} E[I(Y_i-X_i^T\beta -Z_i^T\alpha _\tau (U_i)\le 0)-I(\epsilon _i\le 0)|X_i,Z_i,U_i]. \end{aligned}$$

Following similar proof line as for \(J_n\overset{p}{\rightarrow }0\), one can verify \(\sup _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}'\Vert \overset{p}{\rightarrow }0\). Next, we prove \(P\{\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}''\Vert \ge 4\sqrt{C_{\delta }}\}\rightarrow 1\). Note that \(A_{1n}''=[\frac{1}{n}\sum _{i=1}^nf_{\epsilon }(\xi ^*|X_i,Z_i,U_i)X_iX_i^T] (\beta -\beta _{\tau })\), where \(\xi ^*\) is between 0 and \(X_i^T(\beta -\beta _{\tau })\). By (A1) and (A4), we know \(f_{\epsilon }(\xi ^*|X_1,Z_1,U_1)\ge c\) and \(E(XX^T)\) is positive definite. Observe that \( \Vert A_{1n}''\Vert ^2=(\beta -\beta _{\tau })^T[\frac{1}{n}\sum _{i=1}^nf_{\epsilon } (\xi ^*|X_i,Z_i,U_i)X_iX_i^T]^2(\beta -\beta _{\tau }). \) Hence \(\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}''\Vert \ge c_0\delta \) in probability. Thus \(P\{\inf _{\beta \in \mathcal {B}_\delta ^c}\Vert A_{1n}''\Vert >4\sqrt{C_{\delta }}\}\rightarrow 1\) with \(C_{\delta }=\frac{c_0^2\delta ^2}{16}\). \(\square \)

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Liu, CS., Liang, HY. Bayesian empirical likelihood of quantile regression with missing observations. Metrika (2022). https://doi.org/10.1007/s00184-022-00869-y

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Keywords

  • Bayesian empirical likelihood
  • Missing at random
  • Posterior distribution
  • Quantile regression
  • Variable selection

Mathematics Subject Classification

  • 62C10
  • 62E20