Semi-empiricial Likelihood Confidence Intervals for the Differences of Two Populations Based on Fractional Imputation

Suppose that there are two populations x and y with missing data on both of them, where x has a distribution function F（·） which is unknown and y has a distribution function Gθ（·） with a probability density function gθ（·） with known form depending on some unknown parameter θ. Fractional imputation is used to fill in missing data. The asymptotic distributions of the semi-empirical likelihood ration statistic are obtained under some mild conditions. Then, empirical likelihood confidence intervals on the differences of x and y are constructed.

Joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples

In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also obtained.

EMPIRICAL LIKELIHOOD-BASED INFERENCE IN LINEAR MODELS WITH INTERVAL CENSORED DATA

An empirical likelihood approach to estimate the coefficients in linear model with interval censored responses is developed in this paper. By constructing unbiased transformation of interval censored data,an empirical log-likelihood function with asymptotic X^2 is derived. The confidence regions for the coefficients are constructed. Some simulation results indicate that the method performs better than the normal approximation method in term of coverage accuracies.

Empirical likelihood for first-order mixed integer-valued autoregressive model

In this paper, we not only construct the confidence region for parameters in a mixed integer-valued autoregressive process using the empirical likelihood method, but also establish the empirical log-likelihood ratio statistic and obtain its limiting distribution. And then, via simulation studies we give coverage probabilities for the parameters of interest. The results show that the empirical likelihood method performs very well.

Bootstrap Confidence Intervals for Proportions of Unequal Sized Groups Adjusted for Overdispersion

Group testing is a method of pooling a number of units together and performing a single test on the resulting group. It is an appealing option when few individual units are thought to be infected leading to reduced costs of testing as compared to individually testing the units. Group testing aims to identify the positive groups in all the groups tested or to estimate the proportion of positives (p) in a population. Interval estimation methods of the proportions in group testing for unequal group sizes adjusted for overdispersion have been examined. Lately improvement in statistical methods allows the construction of highly accurate confidence intervals (CIs). The aim here is to apply group testing for estimation and generate highly accurate Bootstrap confidence intervals (CIs) for the proportion of defective or positive units in particular. This study provided a comparison of several proven methods of constructing CIs for a binomial proportion after adjusting for overdispersion in group testing with groups of unequal sizes. Bootstrap resampling was applied on data simulated from binomial distribution, and confidence intervals with high coverage probabilities were produced. This data was assumed to be overdispersed and independent between groups but correlated within these groups. Interval estimation methods based on the Wald, the Logit and Complementary log-log (CLL) functions were considered. The criterion used in the comparisons is mainly the coverage probabilities attained by nominal 95% CIs, though interval width is also regarded. Bootstrapping produced CIs with high coverage probabilities for each of the three interval methods.

Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data

The receiver operating characteristic (ROC) curve has been widely used in scientific research fields. After using the random hot deck imputation, we propose the smoothed empirical likelihood ratio statistic for the ROC curve with missing data. Its asymptotic distribution is a scaled chi-square distribution and empirical likelihood confidence intervals for ROC curves are constructed. The simulation study shows that the proposed interval estimates perform well based on the coverage probability for different sample sizes and response rates.

Confidence Intervals for the Binomial Proportion: A Comparison of Four Methods

This paper presents four methods of constructing the confidence interval for the proportion p of the binomial distribution. Evidence in the literature indicates the standard Wald confidence interval for the binomial proportion is inaccurate, especially for extreme values of p. Even for moderately large sample sizes, the coverage probabilities of the Wald confidence interval prove to be erratic for extreme values of p. Three alternative confidence intervals, namely, Wilson confidence interval, Clopper-Pearson interval, and likelihood interval, are compared to the Wald confidence interval on the basis of coverage probability and expected length by means of simulation.

Semi-empirical Likelihood Confidence Intervals for the Differences of Quantiles with Missing Data

Detecting population （group） differences is useful in many applications, such as medical research. In this paper, we explore the probabilistic theory for identifying the quantile differences .between two populations. Suppose that there are two populations x and y with missing data on both of them, where x is nonparametric and y is parametric. We are interested in constructing confidence intervals on the quantile differences of x and y. Random hot deck imputation is used to fill in missing data. Semi-empirical likelihood confidence intervals on the differences are constructed.

Empirical Likelihood Confidence Intervals for the Differences of Quantiles with Missing Data

Suppose that there are two nonparametric populations x and y with missing data on both of them. We are interested in constructing confidence intervals on the quantile differences of x and y. Random imputation is used. Empirical likelihood confidence intervals on the differences are constructed.

Confidence Interval Estimation of the Correlation in the Presence of Non-Detects

This article deals with correlating two variables that have values that fall below the known limit of detection (LOD) of the measuring device;these values are known as non-detects (NDs). We use simulation to compare several methods for estimating the association between two such variables. The most commonly used method, simple substitution, consists of replacing each ND with some representative value such as LOD/2. Spearman’s correlation, in which all NDs are assumed to be tied at some value just smaller than the LOD, is also used. We evaluate each method under several scenarios, including small to moderate sample size, moderate to large censoring proportions, extreme imbalance in censoring proportions, and non-bivariate normal (BVN) data. In this article, we focus on the coverage probability of 95% confidence intervals obtained using each method. Confidence intervals using a maximum likelihood approach based on the assumption of BVN data have acceptable performance under most scenarios, even with non-BVN data. Intervals based on Spearman’s coefficient also perform well under many conditions. The methods are illustrated using real data taken from the biomarker literature.

Constructing Confidence Regions for Autoregressive-Model Parameters

We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.

Empirical Bayes Prediction in Exponential Distribution

This paper concerns with an empirical Bayes prediction problem in exponential distribution. Using observed samples, we construct a prediction interval for a set of interest which consists of some unobserved samples. Simulation studies with different prior distributions are conducted to examine coverage probability of the prediction interval.

多元广义线性模型经验似然方法分析

针对多元广义线性模型,基于估计相关阵、广义估计方程和经验似然方法,本文构造出经验似然比统计量,此统计量能克服“工作相关阵”方法的误设定问题.在一定的条件下,本文也获得了经验似然比统计量渐近Wilks性质,该结果可用作未知参数向量置信域的构造.最后,通过数值模拟对所提方法的有效性进行验证.

基于经验似然方法对分位数相关系数的区间估计

分位数相关系数是一种度量两个随机变量之间线性相关关系的非对称相关系数,在统计,金融,化学等领域的特征选择问题中都扮演着重要的作用.同时,经验似然作为一种非参数方法,被广泛应用于各类模型的统计推断问题.对于任意两个随机变量,从分位数相关系数的定义出发,建立估计方程,引入代入经验似然方法(PEL)和其修正版本(APEL),分别得到渐近规则化的卡方分布和标准卡方分布,从而得到分位数相关系数的区间估计.数值模拟部分从覆盖概率,置信区间长度和基于区间得分的平均损失三方面比较了两种基于经验似然的方法同其他已有方法的效果.实证分析部分将提出的方法应用于一项来自福布斯排行榜的数据集.

含空间自相关误差的空间自回归模型的调整经验似然推断

本文研究含空间自相关误差的空间自回归模型的调整经验似然推断问题。利用调整经验似然方法,构造出含空间自相关误差的空间自回归模型的调整经验似然比统计量,证明调整经验似然统计量的极限分布为卡方分布,并模拟比较调整经验似然与一般经验似然方法的优劣。模拟结果表明:调整经验似然比一般经验似然置信域的覆盖精度更高,计算速度更快。在样本容量较大时,无论误差项是否服从正态分布,一般经验似然方法和调整经验似然方法均可;在样本容量较小时,推荐使用调整经验似然方法。

Empirical likelihood confidence regions of the parameters in a partially linear single-index model

In this paper, a partially linear single-index model is investigated, and three empirical log-likelihood ratio statistics for the unknown parameters in the model are suggested. It is proved that the proposed statistics are asymptotically standard chi-square under some suitable conditions, and hence can be used to construct the confidence regions of the parameters. Our methods can also deal with the confidence region construction for the index in the pure single-index model. A simulation study indicates that, in terms of coverage probabilities and average areas of the confidence regions, the proposed methods perform better than the least-squares method.

记录值下Frechet模型的参数估计及剩余寿命预测

在记录值样本下,利用经典方法讨论了Frechet分布未知参数的极大似然估计,通过构建枢轴量得到了未知参数的精确置信区间和置信域。根据观测Fisher信息矩阵构造了未知参数的近似置信区间。利用经典方法对元件的剩余寿命进行了预测。最后,通过数值例子计算出了未知参数的点估计和区间估计。

Constructing confidence intervals of extreme rainfall quantiles using Bayesian,bootstrap,and profile likelihood approaches

Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.

EMPIRICAL LIKELIHOOD CONFIDENCE REGION FOR PARAMETERS IN LINEAR ERRORS-IN-VARIABLES MODELS WITH MISSING DATA

The multivariate linear errors-in-variables model when the regressors are missing at random in the sense of Rubin （1976） is considered in this paper. A constrained empirical likelihood confidence region for a parameter β0 in this model is proposed, which is constructed by combining the score function corresponding to the weighted squared orthogonal distance based on inverse probability with a constrained region of β0. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard chi-square distribution. Simulations show that the coverage rate of the proposed confidence region is closer to the nominal level and the length of confidence interval is narrower than those of the normal approximation of inverse probability weighted adjusted least square estimator in most cases. A real example is studied and the result supports the theory and simulation＇s conclusion.

Empirical Likelihood Ratio Confidence Interval for Positively Associated Series

Empirical likelihood is discussed by using the blockwise technique for strongly stationary, positively associated random variables. Our results show that the statistics is asymptotically chi-square distributed and the corresponding confidence interval can be constructed.