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In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter is often associated with poor outcomes and switching to arteriovenous fistula is beneficial, studies have not fully elucidated how the effect of switching of access on outcomes changes over time for patients on dialysis and whether the effect depends on switching time. In this paper, we characterise the switching access type effect on outcomes for haemodialysis patients. This is achieved by using a new class of multiple-index varying-coefficient (MIVC) models. We develop a new estimation procedure for MIVC models based on local linear, profile least-square method and Cholesky decomposition. Monte Carlo simulation studies show excellent finite sample performance. Finally, we analyse the dialysis data using our method. [ABSTRACT FROM PUBLISHER]

In this article, we investigate a new estimation approach for the partially linear single-index model based on modal regression method, where the non parametric function is estimated by penalized spline method. Moreover, we develop an expection maximum (EM)-type algorithm and establish the large sample properties of the proposed estimation method. A distinguishing characteristic of the newly proposed estimation is robust against outliers through introducing an additional tuning parameter which can be automatically selected using the observed data. Simulation studies and real data example are used to evaluate the finite-sample performance, and the results show that the newly proposed method works very well. [ABSTRACT FROM AUTHOR]

Partially varying coefficient single-index models (PVCSIM) are a class of semiparametric regression models. One important assumption is that the model error is independently and identically distributed, which may contradict with the reality in many applications. For example, in the economical and financial applications, the observations may be serially correlated over time. Based on the empirical likelihood technique, we propose a procedure for testing the serial correlation of random error in PVCSIM. Under some regular conditions, we show that the proposed empirical likelihood ratio statistic asymptotically follows a standard χ2distribution. We also present some numerical studies to illustrate the performance of our proposed testing procedure. [ABSTRACT FROM AUTHOR]

This article considers a nonparametric additive seemingly unrelated regression model with autoregressive errors, and develops estimation and inference procedures for this model. Our proposed method first estimates the unknown functions by combining polynomial spline series approximations with least squares, and then uses the fitted residuals together with the smoothly clipped absolute deviation (SCAD) penalty to identify the error structure and estimate the unknown autoregressive coefficients. Based on the polynomial spline series estimator and the fitted error structure, a two-stage local polynomial improved estimator for the unknown functions of the mean is further developed. Our procedure applies a prewhitening transformation of the dependent variable, and also takes into account the contemporaneous correlations across equations. We show that the resulting estimator possesses an oracle property, and is asymptotically more efficient than estimators that neglect the autocorrelation and/or contemporaneous correlations of errors. We investigate the small sample properties of the proposed procedure in a simulation study. [ABSTRACT FROM PUBLISHER]

The varying-coefficient model is an important nonparametric statistical model since it allows appreciable flexibility on the structure of fitted model. For ultra-high dimensional heterogeneous data it is very necessary to examine how the effects of covariates vary with exposure variables at different quantile level of interest. In this paper, we extended the marginal screening methods to examine and select variables by ranking a measure of nonparametric marginal contributions of each covariate given the exposure variable. Spline approximations are employed to model marginal effects and select the set of active variables in quantile-adaptive framework. This ensures the sure screening property in quantile-adaptive varying-coefficient model. Numerical studies demonstrate that the proposed procedure works well for heteroscedastic data. [ABSTRACT FROM PUBLISHER]

This paper is concerned with how to select significant variables in the partially linear varying-coefficient hazard model for multivariate survival data. A new variable selection procedure is proposed to simultaneously estimate the parameters and select variables for the parametric parts. Compared to the profile pseudo-partial likelihood proposed by Cai et al. (2008), the advantage of our method is to be practically feasible and easily implemented. We show that the estimators of both the parametric and nonparametric parts achieve the best convergence rates and establish their asymptotic normality. Moreover, we demonstrate that proposed procedures perform as well as an oracle procedure. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed procedures and a real dataset from the Colon Cancer Study is analyzed for illustration. [ABSTRACT FROM AUTHOR]

Based on the B spline approximation technique and right censored data, we consider the empirical likelihood inference for the index parameters and its partial components in a class of single index hazard regression models. Under some regular conditions, we show that our proposed empirical likelihood ratio statistics follow the standard χ 2 distribution. Some numerical studies are given to illustrate our proposed methodology. [ABSTRACT FROM AUTHOR]

The Student- t regression model is a useful extension of the normal model, which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and providesrobust estimation of means and regression coefficients. In this paper, the varying dispersion Student- t regression model is discussed, in which both the mean and the dispersion depend upon explanatory variables. The problem of interest is simultaneously select significant variables both in mean and dispersion model. A unified procedure which can simultaneously select significant variable is given. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. Both the simulation study and two real data examples are used to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we consider how to yield a robust empirical likelihood estimation for regression models. After introducing modal regression, we propose a novel empirical likelihood method based on modal regression estimation equations, which has the merits of both robustness and high inference efficiency compared with the least square based methods. Under some mild conditions, we show that Wilks' theorem of the proposed empirical likelihood approach continues to hold. Advantages of empirical likelihood modal regression as a nonparametric approach are illustrated by constructing confidence intervals/regions. Two simulation studies and a real data analysis confirm our theoretical findings. [ABSTRACT FROM AUTHOR]

Partial linear single-index model (PLSIM) is a flexible and applicable model when investigating the underlying relationship between the response and the multivariate covariates. Most previous studies on PLSIM concentrated on mean regression, based on least square or likelihood approach. In contrast to this method, in this paper, we propose minimizing average check loss estimation (MACLE) procedure to conduct quantile regression of PLSIM. We construct an initial consistent quantile regression estimator of the parametric part base multi-dimensional kernels, and further promote the estimation efficiency to the optimal rate. We discuss the optimal bandwidth selection method and establish the asymptotic normality of the proposed MACLE estimators. Furthermore, we consider an adaptive lasso penalized variable selection method and establish its oracle property. Simulation studies with various distributed error and a real data analysis are conducted to show the promise of our proposed methods. [ABSTRACT FROM AUTHOR]

In this article, we propose two testing procedures for the serial correlation in single index models by virtue of B spline approximation for unknown single index function. Under some regular conditions, we show that our proposed statistics asymptotically follow normal and χ2distribution. Many numerical studies illustrate that the proposed procedures can perform very well for moderate sample size. [ABSTRACT FROM AUTHOR]

Functional linear concurrent regression model is an important model in functional regression. It is usually assumed that realizations of functional covariate are independent and observed precisely. But in practice, the dependence across different functional sample curves often exists. Moreover, each realization of functional covariate may be contaminated with noise. To address this issue, we propose a novel estimation method, which makes full use of dependence information and filters out the impact of measured noise. Then, we extend the proposed method to partially observed functional data. Under some regular conditions, we establish asymptotic properties of the estimators of the model. Finite-sample performance of our estimation is illustrated by Monte Carlo simulation studies and a real data example. Numerical results reveal that the proposed method exhibits superior performance compared with the existing methods. [ABSTRACT FROM AUTHOR]