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Nonparametric significance testing and group variable selection

Zambom, Adriano Zanin; Akritas, Michael G. - In: Journal of Multivariate Analysis 133 (2015) C, pp. 51-60

In the context of a heteroscedastic nonparametric regression model, we develop a test for the null hypothesis that a subset of the predictors has no influence on the regression function. The test uses residuals obtained from local polynomial fitting of the null model and is based on a test...

Persistent link: https://www.econbiz.de/10011116237

General Properties and Estimation of Conditional Bernoulli Models

Chen, Sean X. - In: Journal of Multivariate Analysis 74 (2000) 1, pp. 69-87

Conditional Bernoulli (in short "CB") models have been recently applied to many statistical fields including survey sampling, logistic regression, case-control studies, lottery, signal processing and Poisson-Binomial distributions. In this paper, we present several general properties of CB...

Persistent link: https://www.econbiz.de/10005106940

Local Walsh-average regression

Feng, Long; Zou, Changliang; Wang, Zhaojun - In: Journal of Multivariate Analysis 106 (2012) C, pp. 36-48

Local polynomial regression is widely used for nonparametric regression. However, the efficiency of least squares (LS) based methods is adversely affected by outlying observations and heavy tailed distributions. On the other hand, the least absolute deviation (LAD) estimator is more robust, but...

Persistent link: https://www.econbiz.de/10011042049

Asymptotic Properties of Backfitting Estimators

Opsomer, Jean D. - In: Journal of Multivariate Analysis 73 (2000) 2, pp. 166-179

as the asymptotically optimal values for the bandwidth parameters, are provided. …

Persistent link: https://www.econbiz.de/10005221485

Nonparametric LAD cointegrating regression

Honda, Toshio - In: Journal of Multivariate Analysis 117 (2013) C, pp. 150-162

We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and error term can be contemporaneously correlated. The asymptotic properties of the Nadaraya–Watson estimator are already examined in the literature. In this paper, we consider nonparametric least...

Persistent link: https://www.econbiz.de/10010665703

SCAD-penalized regression for varying-coefficient models with autoregressive errors

Qiu, Jia; Li, Degao; You, Jinhong - In: Journal of Multivariate Analysis 137 (2015) C, pp. 100-118

Varying-coefficient models are useful extension of classical linear models. This paper is concerned with the statistical inference of varying-coefficient regression models with autoregressive errors. By combining the estimated residuals, the smoothly clipped absolute deviation (SCAD) penalty and...

Persistent link: https://www.econbiz.de/10011263463

Nonparametric estimation of fixed effects panel data varying coefficient models

Rodriguez-Poo, Juan M.; Soberón, Alexandra - In: Journal of Multivariate Analysis 133 (2015) C, pp. 95-122

In this paper, we consider the nonparametric estimation of a varying coefficient fixed effect panel data model. The estimator is based in a within (un-smoothed) transformation of the regression model and then a local linear regression is applied to estimate the unknown varying coefficient...

Persistent link: https://www.econbiz.de/10011116242

A robust and efficient estimation method for single index models

Liu, Jicai; Zhang, Riquan; Zhao, Weihua; Lv, Yazhao - In: Journal of Multivariate Analysis 122 (2013) C, pp. 226-238

Single index models are natural extensions of linear models and overcome the so-called curse of dimensionality. They have applications to many fields, such as medicine, economics and finance. However, most existing methods based on least squares or likelihood are sensitive when there are...

Persistent link: https://www.econbiz.de/10010702795

Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval

Kim, Seonjin; Zhao, Zhibiao; Shao, Xiaofeng - In: Journal of Multivariate Analysis 133 (2015) C, pp. 277-290

involves another level of nonparametric smoothing. In practice, the choice of the extra bandwidth parameter can be difficult …, the inference results can be sensitive to bandwidth selection and the normal approximation can be quite unsatisfactory in …-normalized approach, which is a bandwidth free inference procedure developed for parametric inference, to construct point-wise confidence …

Persistent link: https://www.econbiz.de/10011116246

Tie the straps: Uniform bootstrap confidence bands for semiparametric additive models

Härdle, Wolfgang Karl; Ritov, Ya’acov; Wang, Weining - In: Journal of Multivariate Analysis 134 (2015) C, pp. 129-145

This study considers the theoretical bootstrap “coupling” techniques for nonparametric robust smoothers and quantile regression, and we verify the bootstrap improvement. To handle the curse of dimensionality, a variant of “coupling” bootstrap techniques is developed for additive models...

Persistent link: https://www.econbiz.de/10011189579