共查询到20条相似文献,搜索用时 31 毫秒
1.
Empirical likelihood-based inference in nonlinear regression models with missing responses at random
This paper investigates the estimations of regression parameters and response mean in nonlinear regression models in the presence of missing response variables that are missing with missingness probabilities depending on covariates. We propose four empirical likelihood (EL)-based estimators for the regression parameters and the response mean. The resulting estimators are shown to be consistent and asymptotically normal under some general assumptions. To construct the confidence regions for the regression parameters as well as the response mean, we develop four EL ratio statistics, which are proven to have the χ2 distribution asymptotically. Simulation studies and an artificial data set are used to illustrate the proposed methodologies. Empirical results show that the EL method behaves better than the normal approximation method and that the coverage probabilities and average lengths depend on the selection probability function. 相似文献
2.
In this article, we study the construction of confidence intervals for regression parameters in a linear model under linear process errors by using the blockwise technique. It is shown that the blockwise empirical likelihood (EL) ratio statistic is asymptotically χ2-type distributed. The result is used to obtain EL based confidence regions for regression parameters. The finite-sample performance of the method is evaluated through a simulation study. 相似文献
3.
Empirical Likelihood for First-order Autoregressive Error-in-variable of Models With Validation Data
Shi-hang Yu 《统计学通讯:理论与方法》2014,43(8):1800-1823
In this article, we consider the empirical likelihood for the autoregressive error-in-explanatory variable models. With the help of validation, we first develop an empirical likelihood ratio test statistic for the parameters of interest, and prove that its asymptotic distribution is that of a weighted sum of independent standard χ21 random variables with unknown weights. Also, we propose an adjusted empirical likelihood and prove that its asymptotic distribution is a standard χ2. Furthermore, an empirical likelihood-based confidence region is given. Simulation results indicate that the proposed method works well for practical situations. 相似文献
4.
ABSTRACTThis article develops an adjusted empirical likelihood (EL) method for the additive hazards model. The adjusted EL ratio is shown to have a central chi-squared limiting distribution under the null hypothesis. We also evaluate its asymptotic distribution as a non central chi-squared distribution under the local alternatives of order n? 1/2, deriving the expression for the asymptotic power function. Simulation studies and a real example are conducted to evaluate the finite sample performance of the proposed method. Compared with the normal approximation-based method, the proposed method tends to have more larger empirical power and smaller confidence regions with comparable coverage probabilities. 相似文献
5.
Suppose that we have a nonparametric regression model Y = m(X) + ε with X ∈ Rp, where X is a random design variable and is observed completely, and Y is the response variable and some Y-values are missing at random. Based on the “complete” data sets for Y after nonaprametric regression imputation and inverse probability weighted imputation, two estimators of the regression function m(x0) for fixed x0 ∈ Rp are proposed. Asymptotic normality of two estimators is established, which is used to construct normal approximation-based confidence intervals for m(x0). We also construct an empirical likelihood (EL) statistic for m(x0) with limiting distribution of χ21, which is used to construct an EL confidence interval for m(x0). 相似文献
6.
Zheng-Yan Lin 《统计学通讯:理论与方法》2014,43(19):4088-4102
In this paper, we use blockwise empirical likelihood (EL) technique to construct confidence regions for the parameter of the partial linear models under negatively associated errors. It is shown that the blockwise EL ratio statistic for the parameter of interest is asymptotically χ2-type distributed by employing the large-block and small-block arguments. The result can be used to obtain confidence regions for the parameter of interest. 相似文献
7.
In this article, we discuss the construction of the confidence intervals for distribution functions under negatively associated samples. It is shown that the blockwise empirical likelihood (EL) ratio statistic for a distribution function is asymptotically χ2-type distributed. The result is used to obtain an EL-based confidence interval for the distribution function. 相似文献
8.
Xuerong Chen Yan Chen Alan T.K. Wan Yong Zhou 《Scandinavian Journal of Statistics》2019,46(2):361-388
The generalized method of moments (GMM) and empirical likelihood (EL) are popular methods for combining sample and auxiliary information. These methods are used in very diverse fields of research, where competing theories often suggest variables satisfying different moment conditions. Results in the literature have shown that the efficient‐GMM (GMME) and maximum empirical likelihood (MEL) estimators have the same asymptotic distribution to order n?1/2 and that both estimators are asymptotically semiparametric efficient. In this paper, we demonstrate that when data are missing at random from the sample, the utilization of some well‐known missing‐data handling approaches proposed in the literature can yield GMME and MEL estimators with nonidentical properties; in particular, it is shown that the GMME estimator is semiparametric efficient under all the missing‐data handling approaches considered but that the MEL estimator is not always efficient. A thorough examination of the reason for the nonequivalence of the two estimators is presented. A particularly strong feature of our analysis is that we do not assume smoothness in the underlying moment conditions. Our results are thus relevant to situations involving nonsmooth estimating functions, including quantile and rank regressions, robust estimation, the estimation of receiver operating characteristic (ROC) curves, and so on. 相似文献
9.
In this paper, we study the construction of confidence intervals for a probability density function under a (positively) associated sample by using the blockwise technique. It is shown that the blockwise empirical likelihood (EL) ratio statistic is asymptotically χ2-type distributed. The result is used to obtain EL-based confidence interval for the probability density function. 相似文献
10.
11.
ABSTRACTThis paper proposes an empirical likelihood (EL) method for estimating the GARCH(p, q) models with heavy-tailed errors. Using the kernel smoothing method, we derive a smoothed EL ratio statistic, which yields a smoothed EL estimator. Moreover, we derive a profile EL for the partial parameters in the presence of nuisance parameters. Simulations and empirical results are conducted to illustrate our proposed method. 相似文献
12.
A NEW CALIBRATION METHOD OF CONSTRUCTING EMPIRICAL LIKELIHOOD-BASED CONFIDENCE INTERVALS FOR THE TAIL INDEX 总被引:1,自引:0,他引:1
Empirical likelihood has attracted much attention in the literature as a nonparametric method. A recent paper by Lu & Peng (2002) [Likelihood based confidence intervals for the tail index. Extremes 5, 337–352] applied this method to construct a confidence interval for the tail index of a heavy‐tailed distribution. It turns out that the empirical likelihood method, as well as other likelihood‐based methods, performs better than the normal approximation method in terms of coverage probability. However, when the sample size is small, the confidence interval computed using the χ2 approximation has a serious undercoverage problem. Motivated by Tsao (2004) [A new method of calibration for the empirical loglikelihood ratio. Statist. Probab. Lett. 68, 305–314], this paper proposes a new method of calibration, which corrects the undercoverage problem. 相似文献
13.
In this paper, we investigate empirical likelihood (EL) inference for density-weighted average derivatives in nonparametric
multiple regression models. A simply adjusted empirical log-likelihood ratio for the vector of density-weighted average derivatives
is defined and its limiting distribution is shown to be a standard Chi-square distribution. To increase the accuracy and coverage
probability of confidence regions, an EL inference procedure for the rescaled parameter vector is proposed by using a linear
instrumental variables regression. The new method shares the same properties of the regular EL method with i.i.d. samples.
For example, estimation of limiting variances and covariances is not needed. A Monte Carlo simulation study is presented to
compare the new method with the normal approximation method and an existing EL method. 相似文献
14.
Zhensheng Huang 《统计学通讯:模拟与计算》2016,45(7):2638-2656
In this article, we consider whether the empirical likelihood ratio (ELR) test is applicable to testing for serial correlation in the partially linear single-index models (PLSIM) with error-prone linear covariates. It is shown that under the null hypothesis the proposed ELR statistic follows asymptotically a χ2-distribution with the scale constant and the degrees of freedom. A comparison between the ELR and the normal approximation method is also considered. Both simulated and real data examples are used to illustrate our proposed methodology. 相似文献
15.
ON THE COVERAGE PROBABILITY OF CONFIDENCE INTERVALS IN REGRESSION AFTER VARIABLE SELECTION 总被引:1,自引:1,他引:0
This paper considers a linear regression model with regression parameter vector β. The parameter of interest is θ= aTβ where a is specified. When, as a first step, a data‐based variable selection (e.g. minimum Akaike information criterion) is used to select a model, it is common statistical practice to then carry out inference about θ, using the same data, based on the (false) assumption that the selected model had been provided a priori. The paper considers a confidence interval for θ with nominal coverage 1 ‐ α constructed on this (false) assumption, and calls this the naive 1 ‐ α confidence interval. The minimum coverage probability of this confidence interval can be calculated for simple variable selection procedures involving only a single variable. However, the kinds of variable selection procedures used in practice are typically much more complicated. For the real‐life data presented in this paper, there are 20 variables each of which is to be either included or not, leading to 220 different models. The coverage probability at any given value of the parameters provides an upper bound on the minimum coverage probability of the naive confidence interval. This paper derives a new Monte Carlo simulation estimator of the coverage probability, which uses conditioning for variance reduction. For these real‐life data, the gain in efficiency of this Monte Carlo simulation due to conditioning ranged from 2 to 6. The paper also presents a simple one‐dimensional search strategy for parameter values at which the coverage probability is relatively small. For these real‐life data, this search leads to parameter values for which the coverage probability of the naive 0.95 confidence interval is 0.79 for variable selection using the Akaike information criterion and 0.70 for variable selection using Bayes information criterion, showing that these confidence intervals are completely inadequate. 相似文献
16.
Two examples of absolutely continuous bivariate distributions are given. The first example illustrates the fact that the sum of two random variables can be χ2, one of the variables χ2, the other variable positive but not necessarily χ2. The second example illustrates the fact that the sum of the variables can be χ2, each variable can be χ2, the degrees of freedom add up properly but the two variables need not be independent. 相似文献
17.
Likelihood-ratio tests (LRTs) are often used for inferences on one or more logistic regression coefficients. Conventionally, for given parameters of interest, the nuisance parameters of the likelihood function are replaced by their maximum likelihood estimates. The new function created is called the profile likelihood function, and is used for inference from LRT. In small samples, LRT based on the profile likelihood does not follow χ2 distribution. Several corrections have been proposed to improve LRT when used with small-sample data. Additionally, complete or quasi-complete separation is a common geometric feature for small-sample binary data. In this article, for small-sample binary data, we have derived explicitly the correction factors of LRT for models with and without separation, and proposed an algorithm to construct confidence intervals. We have investigated the performances of different LRT corrections, and the corresponding confidence intervals through simulations. Based on the simulation results, we propose an empirical rule of thumb on the use of these methods. Our simulation findings are also supported by real-world data. 相似文献
18.
Empirical likelihood for probability density functions under negatively associated samples 总被引:1,自引:0,他引:1
Yongsong Qin Yinghua LiQingzhu Lei 《Journal of statistical planning and inference》2011,141(1):373-381
In this paper, we study the construction of confidence intervals for a probability density function under a negatively associated sample by using the blockwise technique. It is shown that the blockwise empirical likelihood (EL) ratio statistic is asymptotically χ2‐type distributed. The result is used to obtain EL based confidence interval on the probability density function. 相似文献
19.
In this article, we employ the jackknife empirical likelihood (JEL) method to construct the confidence regions for the difference of the means of two d-dimensional samples. Compared with traditional EL for the two-sample mean problem, JEL is extremely simpler to use in practice and is more effective in computing. Based on the JEL ratio test, a version of Wilks’ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. The effectiveness of the proposed method is demonstrated by a simulation study and a real data analysis. 相似文献
20.
In this article, we show that the log empirical likelihood ratio statistic for the population mean converges in distribution to χ2(1) as n → ∞ when the population is in the domain of attraction of normal law but has infinite variance. The simulation results show that the empirical likelihood ratio method is applicable under the infinite second moment condition. 相似文献