Optimal estimation in random coefficient regression models

In linear regression models with random coefficients, the score function usually involves unknown nuisance parameters in the form of weights. Conditioning with respect to the sufficient statistics for the nuisance parameter, when the parameter of interest is held fixed, eliminates the nuisance parameters and is expected to give reasonably good estimating functions. The present paper adopts this approach to the problem of estimation of average slope in random coefficient regression models. Four sampling situations are discussed. Some asymptotic results are also obtained for a model where neither the regressors nor the random regression coefficients replicate. Simulation studies for normal as well as non-normal models show that the performance of the suggested estimating functions is quite satisfactory.     

Abnormal behavior of the least squares estimate of multiple regression

An example is given to reveal the abnormal behavior of the least squares estimate of multiple regression. It is shown that the least squares estimate of the multiple linear regression may be “improved” in the sense of weak consistency when nuisance parameters are introduced into the model. A discussion on the implications of this finding is given.     

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本文利用广义p-值和广义置信区间的概念构造 含有三个随机效应的套误差分量模型中方差分量的几种新的精确检验和置信区间, 并讨论它们在尺度变换下的不变性. 模拟结果表明, 基于广义p-值的检验很好地控制了犯第一类错误的概率.     

Non-parametricc-sample tests with regression

For testing that several regression lines are concurrent, a class of rank score tests is proposed. This class includes the usual Wilcoxon and normal scores type of tests. The performance of the proposed tests is shown to be the same as that of rank score tests in the ordinaryc-sample problem.     

关于只有一个变点模型的非参数推断

这里 μ(t)为非随机函数,μ′(t)为 μ(t)的导函数,α,θ.为未知常数,0相似文献   

On one twoepoch linear model with the nuisance parameters

The optimum linear estimators of the useful mean value parameters within a linear regression model with the stable and variable parameters and with the nuisance parameters are derived including their characteristics of accuracy. Some verification of theoretical results is presented.      

Testing for the parametric parts in a single-index varying-coefficient model

Single-index varying-coefficient models (SIVCMs) are very useful in multivariate nonparametric regression.However,there has less attention focused on inferences of the SIVCMs.Using the local linear method,we propose estimates of the unknowns in the SIVCMs.In this article,our main purpose is to examine whether the generalized likelihood ratio (GLR) tests are applicable to the testing problem for the index parameter in the SIVCMs.Under the null hypothesis our proposed GLR statistic follows the chi-squared distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters or functions,which is called as Wilks’ phenomenon (see Fan et al.,2001).A simulation study is conducted to illustrate the proposed methodology.     

Asymptotic theory of some tests for a possible change in the regression slope occurring at an unknown time point

Summary Based on least squares estimators and aligned rank order statistics, some testing procedures for a possible change in the regression slope occurring at an unknown time point are considered. The asymptotic theory of the proposed tests rests on certain invariance principles relating to least squares estimators and aligned rank order statistics, and these are developed here.Work supported by the National Heart, Lung and Blood Institute, Contract NIH-NHLBI-71-2243 from the (U.S.) National Institutes of Health     

Tests for nonparametric parts on partially linear single index models

Tests for nonparametric parts on partially linear single index models are considered in this paper. Based on the estimates obtained by the local linear method, the generalized likelihood ratio tests for the models are established. Under the null hypotheses the normalized tests follow asymptotically the χ2-distribution with the scale constants and the degrees of freedom being independent of the nuisance parameters, which is called the Wilks phenomenon. A simulated example is used to evaluate the performances of the testing procedures empirically.     

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本文利用广义p值和广义置信区间的概念构造含有三个随机效应的Panel数据模型中方差分量的几种新的精确检验和置信区间,并讨论它们在尺度变换下的不变性.通过模拟给出检验的功效和置信区间的覆盖率. 模拟结果表明,广义p值理论方法应用于含有冗余参数的Panel数据模型参数检验问题是灵活而有效的.     

Estimating functions for conditional inference: Many nuisance parameter case

When there is a complete sufficient statistic for the nuisance parameter which depends on the parameter of interest then there are locally optimal unbiased estimating functions, but generally there is no globally optimal estimating function. We consider conditioning on the minimal sufficient statistic for the nuisance parameter and find the conditional linear optimal unbiased estimating function. Since the nuisance parameter is totally eliminated in the conditional model there is no intrinsic problem in setting up conditional tests of significance and confidence intervals. A compromise between conditional and unconditional optimum estimating functions is suggested. The techniques are illustrated on three examples including the well known common means problem. The proposed hypothesis testing and confidence interval procedures work reasonably well for the examples considered.     

Rank procedures for testing subhypotheses in linear regression

Summary In the linear regression modelX _i=α+βc_i+Z_i, we consider the problem of testing the subhypothesis that some (but not all) components of β are equal to 0. A class of asymptotically distribution-free tests based on a quadratic form in aligned rank statistic is studied and the asymptotic relative efficiencies of the proposed tests with respect to the general likelihood ratio test and the test based on least-squares estimates of regression parameters are derived. Asymptotic optimality (à la Wald) is also discussed. Work done under the National Science Foundation Grant MCS 8301409 and NATO Grant 1465.     

混合位置分布中分量参数的统计推断

对混合位置分布族,当混合比已知时,提出了关于分量参数的假设检验和区间估计方法,所提出的方法基于广义枢轴模型.在一定的条件下,检验的实际水平等于名义水平,且各置信域的实际覆盖率等于名义覆盖率.在更一般的场合,检验是相合的,并且各置信域的实际覆盖率趋于名义覆盖率.模拟显示所给的方法是令人满意的.     

Approximation by randomly weighting method in censored regression model

Censored regression (“Tobit”) models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis. This work was supported by National Natural Science Foundation of China (Grant No. 10471136), PhD Program Foundation of the Ministry of Education of China, and Special Foundations of the Chinese Academy of Sciences and University of Science and Technology of China     

Second order asymptotics in nonlinear regression

It is a well known part of statistical knowledge that first order asymptotically efficient procedures can be misleading for moderate sample sizes. Usually this is demonstrated for some popular special cases including numerical comparisons. Typically the situation is worse if nuisance parameters are present. In this paper we give second order asymptotically efficient tests, confidence regions, and estimators for the nonlinear regression model which are based on the least-squares estimator and the residual sum of squares.     

Panel模型中的精确检验和置信区间

利用广义p-值和广义置信区间的概念,研究了Panel模型中未知参数的检验和置信区间问题.对于回归系数,分别考虑了单个情形和多个线性无关情形下的检验和置信区间问题,得到了精确检验和置信区间.对于方差分量,研究了其任意线性组合的检验和置信区间问题,建立了精确检验和置信区间.基于广义p-值和广义置信区间,获取精确检验和置信区间的方法具有计算方便、易应用于小样本问题的特点.最后,分别从理论和数值上研究了这些精确检验和置信区间的统计性质.     

Multivariate linear rank statistics for profile analysis

For some general multivariate linear models, linear rank statistics are used in conjunction with Roy's Union-Intersection Principle to develop some tests for inference on the parameter (vector) when they are subject to certain linear constraints. More powerful tests are designed by incorporating the a priori information on these constraints. Profile analysis is an important application of this type of hypothesis testing problem; it consists of a set of hypothesis testing problem for the p responses q-sample model, where it is a priori assumed that the response-sample interactions are null.     

Testing in linear composite quantile regression models

Composite quantile regression (CQR) can be more efficient and sometimes arbitrarily more efficient than least squares for non-normal random errors, and almost as efficient for normal random errors. Based on CQR, we propose a test method to deal with the testing problem of the parameter in the linear regression models. The critical values of the test statistic can be obtained by the random weighting method without estimating the nuisance parameters. A distinguished feature of the proposed method is that the approximation is valid even the null hypothesis is not true and power evaluation is possible under the local alternatives. Extensive simulations are reported, showing that the proposed method works well in practical settings. The proposed methods are also applied to a data set from a walking behavior survey.     

On generalization of the analysis of variance

The standard analysis of variance procedures were developed and organized primarily in the context of the normal linear model; central to this organization is the orthogonality of components and the use of orthogonal projections. This paper examines two model-type generalizations of the normal linear model: the regression model with nonnormal error and the exponential linear model. Principles of conditioning and measurement are used to develop corresponding analysis-of-variance procedures. In each case a linear fibre or foliation structure replaces orthogonality; however, for the intersection of the two model-types, which is the normal linear model, the two quite-different fibre-foliation structures reduce to a product space structure, which with the appropriate inner product, is the usual orthogonality. For implementation, conditional-marginal densities are involved, the marginalization aspect being the restricting aspect: the marginalization degree is the number of nuisance parameters for the regression model-type and is the complement of the number of free parameters for the exponential model-type. Approximations are available and will be discussed subsequently.University of WaterlooUniversity of Toronto     

Modified Unit Root Tests with Nuisance Parameter Free Asymptotic Distributions

It is well-known that the asymptotic distributions of the Dickey-Fuller (DF) tests for a unit root with linear process errors are not free of nuisance parameters. In this paper, we introduce a consistent estimator for the nuisance parameters and then use it to modify the DF tests, denoted as R-tests. Under fairly mild moment and summability conditions on the errors, we show that the asymptotic distributions of the R-tests are of the same as the Dickey-Fuller distributions. In Monte Carlo experiments, the R-tests are shown to have improved size properties.