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## Robust estimation of generalized partially linear model for longitudinal data with dropouts

### Annals of the Institute of Statistical Mathematics (2016-10-01) 68: 977-1000 , October 01, 2016

In this paper, we study the robust estimation of generalized partially linear models (GPLMs) for longitudinal data with dropouts. We aim at achieving robustness against outliers. To this end, a weighted likelihood method is first proposed to obtain the robust estimation of the parameters involved in the dropout model for describing the missing process. Then, a robust inverse probability-weighted generalized estimating equation is developed to achieve robust estimation of the mean model. To approximate the nonparametric function in the GPLM, a regression spline smoothing method is adopted which can linearize the nonparametric function such that statistical inference can be conducted operationally as if a generalized linear model was used. The asymptotic properties of the proposed estimator are established under some regularity conditions, and simulation studies show the robustness of the proposed estimator. In the end, the proposed method is applied to analyze a real data set.

## On influence diagnostic in univariate elliptical linear regression models

### Statistical Papers (2003-01-01) 44: 23-45 , January 01, 2003

We discuss in this paper the assessment of local influence in univariate elliptical linear regression models. This class includes all symmetric continuous distributions, such as normal, Student-t, Pearson VII, exponential power and logistic, among others. We derive the appropriate matrices for assessing the local influence on the parameter estimates and on predictions by considering as influence measures the likelihood displacement and a distance based on the Pearson residual. Two examples with real data are given for illustration.

## Optimal designs with string property under asymmetric errors and SLS estimation

### Statistical Papers (2016-08-24): 1-14 , August 24, 2016

We consider the optimal design problem when the design space consists of binary vectors with a string property, i.e., a single stretch of ones. This is done in the framework of second-order least squares estimation which is known to outperform ordinary least squares estimation when the error distribution is asymmetric. Analytical as well as computational results on optimal design measures, under the *D*- and *A*-criteria, are obtained. The issue of robustness to the unknown skewness parameter of the error distribution is also explored. Finally, we present several procedures which entail *N*-run designs that are highly efficient, if not optimal.

## Robust canonical correlations: A comparative study

### Computational Statistics (2005-06-01) 20: 203-229 , June 01, 2005

### Summary

Several approaches for robust canonical correlation analysis will be presented and discussed. A first method is based on the definition of canonical correlation analysis as looking for linear combinations of two sets of variables having maximal (robust) correlation. A second method is based on alternating robust regressions. These methods are discussed in detail and compared with the more traditional approach to robust canonical correlation via covariance matrix estimates. A simulation study compares the performance of the different estimators under several kinds of sampling schemes. Robustness is studied as well by breakdown plots.

## Identifying Gene-Environment Interactions with a Least Relative Error Approach

### Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics (2016-01-01): 305-321 , January 01, 2016

For complex diseases, the interactions between genetic and environmental risk factors can have important implications beyond the main effects. Many of the existing interaction analyses conduct marginal analysis and cannot accommodate the joint effects of multiple main effects and interactions. In this study, we conduct joint analysis which can simultaneously accommodate a large number of effects. Significantly different from the existing studies, we adopt loss functions based on relative errors, which offer a useful alternative to the “classic” methods such as the least squares and least absolute deviation. Further to accommodate censoring in the response variable, we adopt a weighted approach. Penalization is used for identification and regularized estimation. Computationally, we develop an effective algorithm which combines the majorize-minimization and coordinate descent. Simulation shows that the proposed approach has satisfactory performance. We also analyze lung cancer prognosis data with gene expression measurements.

## Partial least squares classification for high dimensional data using the PCOUT algorithm

### Computational Statistics (2013-04-01) 28: 771-788 , April 01, 2013

Classification of samples into two or multi-classes is to interest of scientists in almost every field. Traditional statistical methodology for classification does not work well when there are more variables (*p*) than there are samples (*n*) and it is highly sensitive to outlying observations. In this study, a robust partial least squares based classification method is proposed to handle data containing outliers where
$$n\ll p.$$
The proposed method is applied to well-known benchmark datasets and its properties are explored by an extensive simulation study.

## Robust estimation of multivariate regression model

### Statistical Papers (2009-01-01) 50: 81-100 , January 01, 2009

This paper studies robust estimation of multivariate regression model using kernel weighted local linear regression. A robust estimation procedure is proposed for estimating the regression function and its partial derivatives. The proposed estimators are jointly asymptotically normal and attain nonparametric optimal convergence rate. One-step approximations to the robust estimators are introduced to reduce computational burden. The one-step local M-estimators are shown to achieve the same efficiency as the fully iterative local M-estimators as long as the initial estimators are good enough. The proposed estimators inherit the excellent edge-effect behavior of the local polynomial methods in the univariate case and at the same time overcome the disadvantages of the local least-squares based smoothers. Simulations are conducted to demonstrate the performance of the proposed estimators. Real data sets are analyzed to illustrate the practical utility of the proposed methodology.

## Comments on: High-dimensional simultaneous inference with the bootstrap

### TEST (2017-12-01) 26: 720-728 , December 01, 2017

The authors should be congratulated on their insightful article proposing forms of residual and paired bootstrap methodologies in the context of simultaneous testing in sparse and high-dimensional linear models. We appreciate the clear exposition of their work, and the effectiveness of the proposed method. The authors advocate for the bootstrap of a complete high-dimensional estimate rather than the linearized part of the test statistic. We appreciate the opportunity to comment on several aspects of this article.

## Testing for cointegration using induced-order statistics

### Computational Statistics (2008-01-01) 23: 131-151 , January 01, 2008

In this paper we explore the usefulness of induced-order statistics in the characterization of integrated series and of cointegration relationships. We propose a non-parametric test statistic for testing the null hypothesis of two independent random walks against wide cointegrating alternatives including monotonic nonlinearities and certain types of level shifts in the cointegration relationship. We call our testing device the induced-order Kolmogorov–Smirnov cointegration test (KS), since it is constructed from the induced-order statistics of the series, and we derive its limiting distribution. This non-parametric statistic endows the test with a number of desirable properties: invariance to monotonic transformations of the series, and robustness for the presence of important parameter shifts. By Monte Carlo simulations we analyze the small sample properties of this test. Our simulation results show the robustness of the induced order cointegration test against departures from linear and constant parameter models.

## Bayesian Perturbation Diagnostics and Robustness

### Bayesian Analysis in Statistics and Econometrics (1992-01-01) 75: 289-301 , January 01, 1992

A Bayesian analysis may depend critically on the modeling assumptions which include prior, likelihood and loss function. A model that has been judged adequate in previous more or less similar situations may be assumed to be the standard. However one ought to consider the effect of perturbing the standard model in potentially conceivable directions especially if graphical procedures indicate the standard may only be marginally adequate. We discuss a variety of perturbation models and Bayesian diagnostics that can be helpful in a local or a more global analysis of the robustness of the sample.