用Excel软件实现最大似然估计数值计算的方法

本文给出用Excel软件实现最大似然估计数值计算求解的方法。内容包括对似然方程求数值解、直接对似然函数或对数似然函数求极大值以及分组数据最大似然估计的数值计算。     

分组数据情形下对数正态分布参数的最大似然估计

我们研究了分组数据情形下对数正态分布所含参数的最大似然估计存在且唯一的充要条件，进而得到了最大似然估计具有强相合性及收敛速度服从重对数律的结论。     

一种惩罚最大似然方法估计混合回归模型

本文考虑具有正态误差假设下混合回归模型的参数估计问题.由于似然函数的无界性,混合回归模型普通的最大似然估计不存在.本文提出一种惩罚最大似然方法来估计混合回归模型的参数,证明惩罚最大似然估计量(penalized maximum likelihood estimation, PMLE)具有强相合和渐近正态性.通过深入模拟研究,从估计精确性角度看,惩罚最大似然估计量有很好的表现.本文还给出一个音调感知的例子来说明理论结果的应用.     

截尾寿命试验中参数最大似然估计的重对数律

本文对于包含定数和定时截尾寿命试验的混合型寿命试验，研究了分布参数的最大似然估计．基于截尾数据，证明了最大似然估计的收敛速度符合重对数律．     

随机截尾情形下Rayleigh分布参数的最大似然估计

研究了随机截尾情形下Rayleigh分布参数的最大似然估计,研究了最大似然估计的存在唯一性;在很一般的条件下证明了估计的强、弱相合性和渐近正态性.     

分组数据参数的单边估计与检验

设ｘ_０＜ｘ_１＜…＜ｘ_ｋ－１＜ｘ_ｋ是分布函数Ｆ（ｘ－θ）支撑集上的分点．令ｎ_ｉ表示落入区间（ｘ_ｉ－１,ｘ_ｉ）的观测值个数,并称其为分组数据．本文讨论了θ在θ≥θ０下的最大似然估计存在且唯一的条件及其渐近性质;给出了利用分组数据求最大似然估计的方法．最后;讨论了θ的单边检验问题．     

Lomax分布极大似然估计的两点研究

本文研究了Lomax分布参数极大似然估计的存在性和估计量的收敛性问题.利用严格的分析法和中心极限定理,获得了Lomax分布极大似然估计的存在性和估计量的渐近正态分布的结果,进一步推广到了有缺失数据的两个Lomax总体中,参数的极大似然估计有强相合性和渐近正态性.     

一个简化的新Laplace AR(1)模型 参数估计及其渐近性质

研究了一个简化的新的Laplace AR(1)模型参数的条件最小二乘估计和最大拟似然估计,并讨论了它们的强相合性和渐近正态性.通过数值模拟和实际例子,说明了最大拟似然估计及模型的优越性.     

纵向数据下部分非线性模型的广义经验似然推断

本文研究了纵向数据下部分非线性模型中未知参数的置信域的构造.利用经验似然方法,构造了非线性函数中未知参数的广义对数经验似然比统计量,证明了其渐近于卡方分布.同时,得到了未知参数的最大经验似然估计,并证明了其渐近正态性.     

混合双重广义线性模型的参数估计

在实际应用中,不同类别的数据统计特性存在差异,所以对异质总体的研究非常有必要.基于总体一,二阶矩存在,利用双重广义线性模型对异质总体的不同子类数据的均值和散度同时建模,研究提出了混合双重广义线性模型.然后,利用EM算法构造了模型参数的最大扩展拟似然估计和最大伪似然估计.最后,通过随机模拟和实例研究,结果表明模型和方法的有效性和有用性.     

Maximum likelihood inference for the Cox regression model with applications to missing covariates

In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model [D.R. Cox, Regression models and life tables (with discussion), Journal of the Royal Statistical Society, Series B 34 (1972) 187–220; D.R. Cox, Partial likelihood, Biometrika 62 (1975) 269–276] both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology.     

Pólya分布在气候统计中的应用

Pólya分布在气候统计中常用来拟合雾、雷暴等.本文给出了Pólya分布总体在全样本场合下参数的矩估计和极大似然估计,并研究了估计的存在性,并通过大量的Monte Carlo模拟说明了估计的精度,认为在样本较大的情形下极大似然估计优于矩估计.最后通过具体的雾与雷暴等气候统计数据说明本文方法的可行性.     

Estimating complex covariance by observing two variables at a time

The estimation of covariance matrices is central in array signal processing systems. This note addresses complex covariance estimation for the situation, where the complex data are available only as independent pairwise sets (observations) corresponding to individual elements of the matrix. The formulation for the empirical estimate and the normal maximum likelihood estimate is developed for the general case of different sample sizes for each observation. The approach allows, for example, the estimate of the p by p covariance matrix of a p-port sensor array from a two-port measurement instrument.     

The efficiency criteria problem for stochastic processes

This paper is concerned with the problem of finding a suitable (asymptotic) efficiency criterion for inference concerning parameters of stochastic processes. Special attention is aid to conditional exponential families of stochastic processes and to three tests based on the maximum likelihood estimate as well as to the likelihood ratio test. A contiguity calculation is used to show that a previously suggested criterion is inadequate and itself provides a partial solution to the problem. A heuristic argument is also put forward to support a proposition implying the optimality of the maximum likelihood estimate in a certain sense. Two examples which illustrate the theory are discussed.     

非线性回归模型的经验似然诊断

经验似然方法已经被广泛用于线性模型和广义线性模型.本文基于经验似然方法对非线性回归模型进行统计诊断.首先得到模型参数的极大经验似然估计;其次基于经验似然研究了三种不同的影响曲率度量;最后通过一个实际例子,说明了诊断方法的有效性.     

Burr Type XII 分布的统计推断

该文讨论了两参数 Burr Type XII 分布基于逐次定数截尾样本的参数估计, 导出了有关参数的点估计和区间估计. 我们利用模拟方法对所给点估计和参数的最大似然估计作了比较, 模拟结果显示所给点估计优于常用的最大似然估计. 最后, 用一个实际例子说明本文所给方法.     

Statistical Analysis of Series System for Masked Data under Successive Censored Life Test

In this paper, considering series system of masked data under simple successive censored and multiple successive censored life test, the likelihood function and maximum likelihood estimate are respectively proposed for series system composed of two units under two kinds of situations. One is the series system composed of two units with constant failure rate, and the other is the series system composed of two units with linear failure rate through the origin. The approximate interval estimates of parameters are given by using the method of likelihood ratio. Besides, the examples show the feasibility of the methods through Monte-Carlo simulations.     

Estimation and inference for dependence in multivariate data

In this paper, a new measure of dependence is proposed. Our approach is based on transforming univariate data to the space where the marginal distributions are normally distributed and then, using the inverse transformation to obtain the distribution function in the original space. The pseudo-maximum likelihood method and the two-stage maximum likelihood approach are used to estimate the unknown parameters. It is shown that the estimated parameters are asymptotical normally distributed in both cases. Inference procedures for testing the independence are also studied.     

指数分布串—并联混合系统产品的统计分析

给出单元寿命服从同一指数分布的串-并联混合系统产品参数的矩估计和极大似然估计,并通过大量Monte-Carlo模拟比较了估计的精度,得到在样本容量小于35时矩估计优于极大似然估计,而样本容量不小于35时极大似然估计优于矩估计.另外,还给出了参数的精确区间估计与近似区间估计,并通过大量Monte-Carlo模拟考察了区间估计的精度.     

Generalized Pseudo-Likelihood Estimates for Markov Random Fields on Lattice

In this paper we generalize Besag's pseudo-likelihood function for spatial statistical models on a region of a lattice. The correspondingly defined maximum generalized pseudo-likelihood estimates (MGPLEs) are natural extensions of Besag's maximum pseudo-likelihood estimate (MPLE). The MGPLEs connect the MPLE and the maximum likelihood estimate. We carry out experimental calculations of the MGPLEs for spatial processes on the lattice. These simulation results clearly show better performances of the MGPLEs than the MPLE, and the performances of differently defined MGPLEs are compared. These are also illustrated by the application to two real data sets.