Asymptotic Properties of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Diverging Number of Covariates

In this paper, for the generalized linear models (GLMs) with diverging number of covariates, the asymptotic properties of maximum quasi-likelihood estimators (MQLEs) under some regular conditions are developed. The existence, weak convergence and the rate of convergence and asymptotic normality of linear combination of MQLEs and asymptotic distribution of single linear hypothesis test statistics are presented. The results are illustrated by Monte-Carlo simulations.     

Profile Statistical Inference for Partially Linear Additive Models with a Diverging Number of Parameters

This paper considers partially linear additive models with the number of parameters diverging when some linear constraints on the parametric part are available. This paper proposes a constrained profile least-squares estimation for the parametric components with the nonparametric functions being estimated by basis function approximations. The consistency and asymptotic normality of the restricted estimator are given under some certain conditions. The authors construct a profile likelihood ratio test statistic to test the validity of the linear constraints on the parametric components,and demonstrate that it follows asymptotically chi-squared distribution under the null and alternative hypotheses. The finite sample performance of the proposed method is illustrated by simulation studies and a data analysis.     

ASSESSMENT OF LOCAL INFLUENCE FOR F-TESTIN LINEAR REGRESSION MODEL

1.IntroductionTheaimofthediagnosticsandinfluenceanalysisistoidentifytheoutliersorinfluelltialobservationsthatsubstantiallyaffecttheparameterestimatesandstatisticalinference.Therelatedworkthathasreceivedconsiderableattentioninlinearregressionmodelistheinfluenceontheregressioncoefficients(see[l--4]).Theinfluencefortheotheraspectsoflinearregressionmodelsuchasconfidenceregionshasalsobeenconsideredby[5].However,theinfluenceonFiestreceivedlessattentionasweknowtheF-testisverysensitivetotheinfluentia…     

Efficient estimation of seemingly unrelated additive nonparametric regression models

This paper is concerned with the estimating problem of seemingly unrelated (SU) nonparametric additive regression models. A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components, which takes both of the additive structure and correlation between equations into account. The asymptotic normality of the derived estimators are establishedi. The authors also show they own some advantages, including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property, which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. Applying the proposed procedure to a real data set is also made.     

Rank-Based Test for Partial Functional Linear Regression Models

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models. Based on a rank score function, the authors develop a rank test using functional principal component analysis, and establish the asymptotic properties of the resulting test under null and local alternative hypotheses. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.     

Robust Estimation for Partial Functional Linear Regression Model Based on Modal Regression

This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression, which combines the common linear model with the functional linear regression model. The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions while performs no worse than the least-square-based estimation method for normal error cases. The slope function is fitted by B-spline. Under suitable conditions, the authors obtain the convergence rates and asymptotic normality of the estimators. Finally, simulation studies and a real data example are conducted to examine the finite sample performance of the proposed method. Both the simulation results and the real data analysis confirm that the newly proposed method works very well.     

The Asymptotic Properties of Scad Penalized Generalized Linear Models with Adaptive Designs

This paper discusses the asymptotic properties of the SCAD (smoothing clipped absolute deviation) penalized quasi-likelihood estimator for generalized linear models with adaptive designs, which extend the related results for independent observations to dependent observations. Under certain conditions, the authors proved that the SCAD penalized method correctly selects covariates with non-zero coefficients with probability converging to one, and the penalized quasi-likelihood estimators of non-zero coefficients have the same asymptotic distribution they would have if the zero coefficients were known in advance. That is, the SCAD estimator has consistency and oracle properties. At last, the results are illustrated by some simulations.

     

Empirical Likelihood Test for Regression Coefficients in High Dimensional Partially Linear Models

Journal of Systems Science and Complexity - This paper considers tests for regression coefficients in high dimensional partially linear Models. The authors first use the B-spline method to estimate...     

ON ASYMPTOTIC NORMALITY OF PARAMETERS IN MULTIPLE LINEAR ERRORS－IN－VARIABLES MODEL

This paper studies the parameter estimation of multiple dimensional linear errors-in-variables (EV) models in the case where replicated observations are available in some experimental points. Asymptotic normality is established under mild conditions, and the parameters entering the asymptotic variance are consistently estimated to render the result useable in the construction of large-sample confidence regions.     

Test for Heteroscedasticity in Partially Linear Regression Models

Testing heteroscedasticity determines whether the regression model can predict the dependent variable consistently across all values of the explanatory variables. Since the proposed tests could not detect heteroscedasticity in all cases, more precisely in heavy-tailed distributions, the authors established new comprehensive test statistic based on Levene's test. The authors built the asymptotic normality of the test statistic under the null hypothesis of homoscedasticity based on the recent theory of analysis of variance for the infinite factors level. The proposed test uses the residuals from a regression model fit of the mean function with Levene's test to assess homogeneity of variance. Simulation studies show that our test yields better than other methods in almost all cases even if the variance is a nonlinear function. Finally, the proposed method is implemented through a real data-set.     

Inference on coefficient function for varying-coefficient partially linear model

One important model in handling the multivariate data is the varying-coemcient partially linear regression model.In this paper,the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not.It is showed that the normalized proposed test follows asymptoticallyχ~2-distribution and the Wilks phenomenon under the null hypothesis,and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing.Some simulation studies illustrate that the test works well.     

ASYMPTOTIC PROPERTIES OF MLE IN EXPONENTIAL FAMILY NONLINEAR MODELS

1.IntroductionSupposethattherandomvariablesyi5y2,'',y.areindependentandeachyihasdensity:withrespecttoaa-finitemeasurev,whereb,c,]areknownl'unctions;fiisaknownq-vectordefinedinX,Pistheunknownp-vectorparameterdefinedinB,oiisthenaturalparameter,andaisascaleparameter.Then(1)iscalledtheexponentialfamilynonlinear..dels[2]whicharenaturalextensionofgeneralizedlinearmodelsandnormalnonlinearmodels.Inthispaper,wegeneralizetheasymptoticresultof[1]and[3,4]toexponentialfamilynonlinearmodels.Section2gives…     

机理变化型协整变结构检验

将变结构协整分为参数变化型协整、部分变化型协整和机理变化型协整。对于机理变化型协整变结构,提出了基于Chow检验统计量的变结构协整检验和建模方法,证明了检验统计量的渐进性质。讨论了变结构点的估计方法与建模步骤。     

ALL ADMISSIBLE LINEAR ESTIMATES OF REGRESSION COEFFICIENTS UNDER MATRIX LOSS

The necessary and sufficient conditions for a linear estimator of a linear estimable functionof regression coefficients in a general fixed effects linear model with the assumptions of normality to beadmissible in the class of all estimators under matrix liss function are given.For a general randomeffects or mixted effects linear model the necessary and sufficient conditions are obtained too.     

Approximate conditional likelihood for generalized linear models with general missing data mechanism

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate. Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.     

Learning Granger causality graphs for multivariate nonlinear time series

An information theory method is proposed to test the. Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the multivariate time series, and the directed edges denote causal dependence, while the undirected edges reflect the instantaneous dependence. The presence of the edges is measured by a statistics based on conditional mutual information and tested by a permutation procedure. Furthermore, for the existed relations, a statistics based on the difference between general conditional mutual information and linear conditional mutual information is proposed to test the nonlinearity. The significance of the nonlinear test statistics is determined by a bootstrap method based on surrogate data. We investigate the finite sample behavior of the procedure through simulation time series with different dependence structures, including linear and nonlinear relations.     

Recurrences for Callan’s Generalization of Narayana Polynomials

By using Chen, Hou and Mu’s extended Zeilberger algorithm, the authors obtain two recurrence relations for Callan’s generalization of Narayana polynomials. Based on these recurrence relations, the authors further prove the real-rootedness and asymptotic normality of Callan’s Narayana polynomials.

     

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using Bsplines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.     

Partially function linear error-in-response models with validation data

This paper considers partial function linear models of the form Y =∫X(t)β(t)dt + g(T)with Y measured with error. The authors propose an estimation procedure when the basis functions are data driven, such as with functional principal components. Estimators of β(t) and g(t) with the primary data and validation data are presented and some asymptotic results are given. Finite sample properties are investigated through some simulation study and a real data application.     

LIMIT THEOREMS AND OPTIMAL DESIGN WITH ADAPTIVE URN MODELS

In this paper we study urn model, using some available estimates of successes probabilities, and adding particle parameter, we establish adaptive models. We obtain some strong convergence theorems, rates of convergence, asymptotic normality of components in the urn, and estimates. With these asymptotical results, we show that the adaptive designs given in this paper are asymptotically optimal designs.