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We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentially β - mixing errors, in which the number of regressors p is large, even much larger than the sample size n . [ABSTRACT FROM AUTHOR]

In this work we focus on functional coefficient regression (FCR) models. Here we study the estimation of FCR models by splines, with autoregressive errors and show the rates of convergence of the proposed estimator. The importance of taking into account the correlation is assessed via simulation studies and multi-step ahead forecasts for a real data set. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study -estimation for the partially linear model under monotonic constraints. We use monotone -splines to approximate the monotone nonparametric function. We show the large sample properties of the resulting estimators. The proposed estimator of parameter part is root- consistent, and asymptotically normal and the estimator for the nonparametric component achieves the optimal convergence rate. A simulation study is conducted to evaluate the finite sample performance of the method. The proposed procedure is illustrated by an air pollution study. [Copyright &y& Elsevier]

Abstract: The augmented inverse probability weighted (AIPW) estimator employing the optimal augmentation term is more efficient than the inverse probability weighted (IPW) estimator. However, the AIPW estimator could lose substantial efficiency compared to the IPW estimator when the optimal augmentation term is incorrectly modeled. We propose a modified AIPW (MAIPW) estimator by adapting Tan’s (2010b) “tilde” estimator, which was proposed for structural models, for regression models with missing data. When the missing mechanism is correctly modeled, the proposed MAIPW estimator is more efficient than the IPW estimator, and is more efficient than the AIPW estimator using the same augmentation term, except when the augmentation term is a correct model for the optimal one, in which case both MAIPW and AIPW estimators attain the semiparametric efficiency bound, thus are equally efficient. In addition, like the AIPW estimator, the MAIPW estimator is doubly robust. Through simulation experiments, we compare numerical performances of the MAIPW estimator and some other estimators that attempt to improve efficiency upon the IPW estimator. [Copyright &y& Elsevier]

Abstract: Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed for these situations, but accelerated failure time models have the particularly attractive feature that they are preserved when going from the backward recurrence times to the underlying survival distribution of interest. This simple fact has recently been noticed in a sociological context and is here illustrated by a study of current duration of time to pregnancy. [Copyright &y& Elsevier]

Abstract: We study the large-sample properties of the penalized maximum likelihood estimator of a multivariate stochastic regression model with contemporaneously correlated data. The penalty is in terms of the square norm of some (vector) linear function of the regression coefficients. The model subsumes the so-called common transfer function model useful for extracting common signals in a panel of short time series. We show that, under mild regularity conditions, the penalized maximum likelihood estimator is consistent and asymptotically normal. The asymptotic bias of the regression coefficient estimator is also derived. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to study the change point estimation problem in multi-phase regression models. This is a non-regular statistical estimation; thus the asymptotic distribution of the maximum likelihood estimator is verified by means of the weak convergence of the likelihood ratio process. These weak convergence results differ depending on the jump size of the regression function. [Copyright &y& Elsevier]

Abstract: The method of least squares provides the most widely used algorithm for fitting a linear model. A variety of nonparametric procedures have been developed that are designed to be robust against model violations and resistant against aberrant points. One such method introduced by Theil [1950. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Proc. Ned. Akad. Wet. 53, 386–392, 521–525, 1397–1412] is based on pairwise estimates. There are many examples in which the data are nonlinear, and in particular, where a quadratic fit may be more appropriate. We here propose a nonparametric method for fitting a quadratic regression. [Copyright &y& Elsevier]

Abstract: This note studies the () spurious regression model for the special case where the weight matrix is normalized and has equal elements, and where the nonstationarity is caused by near unit roots. It shows that spurious spatial regression will not occur in a spatially autoregressive (SAR) model when the spatial weight matrix is row-normalized and has equal weights. In fact, the asymptotic distribution of the OLS estimate will always converge to its true value zero. The only condition required is that the spatial coefficients of the dependent and independent variables be both less than 1, which is a requirement for the SAR model to be an equilibrium model. [ABSTRACT FROM AUTHOR]