The semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modelling, but most existing methods were built on either the least-square or likelihood-based methods, which very sensitive to outliers and their efficiency may be significantly reduced for heavy tail error distribution. This paper proposes a new efficient robust estimation procedure SPLVCM based modal regression. We establish asymptotic normality of proposed estimators both parametric...

The beta regression models are commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). In this paper, we consider issue of variable selection for with varying dispersion (VBRM), which both mean and depend upon predictor variables. Based on a penalized likelihood method, consistency oracle property estimators established. Following coordinate descent algorithm idea generalized linear models, develop new procedure VBRM, can efficiently...

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties proposed estimator are derived. Furthermore, variable selection procedure QR SIM by combining with adaptive LASSO penalized to get sparse index parameter. oracle established. Simulations various non-normal errors conducted demonstrate finite sample performance and procedure. illustrate analyzing real data set.

The group Lasso is a penalized regression method, used in problems where the covariates are partitioned into groups to promote sparsity at level [27 M. Yuan and Y. Lin, Model selection estimation with grouped variables, J. R. Stat. Soc. Ser. B 68 (2006), pp. 49–67. doi: 10.1111/j.1467-9868.2005.00532.x[Crossref] , [Google Scholar]]. Quantile Lasso, natural extension of quantile [25 Wu Liu, Variable regression, Statist. Sinica 19 (2009), 801–817.[Web Science ®] Scholar]], good alternative...

We consider quantile regression incorporating polynomial spline approximation for single-index coefficient models. Compared to mean regression, this class of models is more technically challenging and has not been considered before. use a check loss minimization approach employed projection/orthogonalization technique deal with the theoretical challenges. previously used kernel estimation approach, which was developed only, computationally expedient directly produces smooth estimated curve....

Abstract Peak power load forecasting is a key part of the commercial operation energy industry. Although various methods and technologies have been put forward tested in practice, growing subject tolerance for abnormal accidents to develop robust peak models. In this paper, we propose smooth non‐convex support vector regression method, which improves robustness model by adjusting adaptive control loss values parameters reducing negative impact outliers or noise on decision function. A...

AbstractLongitudinal data arise frequently in many economic studies and epidemiological research. In this paper, we investigate the partially linear additive model for longitudinal framework of quantile regression. To incorporate within-subject correlation, develop an estimation procedure using quadratic inference function (QIF) polynomial spline approximation unknown nonparametric functions. The theoretical properties resulting estimators are established, where functions achieve optimal...

Proportion data with support lying in the interval [0,1] are a commonplace various domains of medicine and public health. When these available as clusters, it is important to correctly incorporate within‐cluster correlation improve estimation efficiency while conducting regression‐based risk evaluation. Furthermore, covariates may exhibit nonlinear relationship (proportion) responses quantifying disease status. As an alternative existing classical methods for modeling proportion (such...

The aim of this paper is to explore variable selection approaches in the partially linear proportional hazards model for multivariate failure time data. A new penalised pseudo-partial likelihood method proposed select important covariates. Under certain regularity conditions, we establish rate convergence and asymptotic normality resulting estimates. We further show that procedure can correctly true submodel, as if it was known advance. Both simulated real data examples are presented...

In this paper, the semi varying coefficient zero-inflated generalized Poisson model is discussed based on penalized log-likelihood. All functions are fitted by spline (P-spline), and Expectation-maximization algorithm used to drive these estimators. The estimation approach rapid computationally stable. Under some mild conditions, consistency asymptotic normality of resulting estimators given. score test statistics about dispersion parameter P-spline estimation. Both simulated real data...