论文检索

可靠性分析中一类多项分布的最大似然估计AI 解读

Summary Let Xi, i = 1, …,k be independent Bernoulli variables, with Xi ~ B(1, pi). Let Y = σ Xi, and consider a multinomial experiment based on n independent, identically distributed observations Y 1,…, Yn. This model is identifiable in the ordered parameter vector (p (1),…, p (k)), where p(i -1) ≤ p(i), and arises in reliability experiments in which k components in parallel have potentially different probabilities of failure. The family of multinomial distributions with the…

Identifiability of finite mixtures of von Mises distributions is proved.

线性时间序列模型检验的一种方法AI 解读

The purpose of this paper is to develop diagnostic tests for open-loop transfer function models with autoregressive-moving average stochastic disturbances using the efficient scores procedure. The tests proposed are asymptotically equivalent to those based upon the likelihood ratio principle and have the advantage that they do not involve a heavy computational burden. Consideration is given both to the standard case of nonsingular information matrices and to the situation…

含大值单位的简单随机样本总体总量估计AI 解读

The problem considered is the estimation of the population total of some characteristic from a simple random sample containing a few large or extreme observations. The effect of these large units in the sample is to distort the estimate of the population total. It is therefore important to correct the weights for such units or deflate their values at the estimation stage once they have been sampled and identified as unusually large units. In this paper, three estimators that…

S 阵列与 Box–Jenkins ARMA 模型识别方法的关系AI 解读

This paper investigates an extension of the partial autocorrelation function, which we call the generalized partial autocorrelation function. These generalized partial autocorrelations, which are not true correlations except when p = 0, are useful in examining the relationship between the S array method of Gray, Kelley, and McIntire (1978) and the Box-Jenkins approach to ARMA model identification. Also, the generalized partial autocorrelation is shown to be a useful model…

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

含大型单位的简单随机样本下总体总量的若干估计量AI 解读

The problem considered is the estimation of the population total of some characteristic from a simple random sample containing a few large or extreme observations. The effect of these large units in the sample is to distort the estimate of the population total. It is therefore important to correct the weights for such units or deflate their values at the estimation stage once they have been sampled and identified as unusually large units. In this paper, three estimators that…

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

摘要暂缺

似然比识别检验统计量的精确密度与近似临界域AI 解读

The exact finite-sample density function of the likelihood ratio (LR) test of overidentifying restrictions in linear equations with any number of included endogenous and exogenous variables is presented, along with its derivation. Properties of the density function and the LR test are presented and discussed. A new approximation for the LR test critical region is proposed and studied. 1. INTRODUCTION AND SUMMARY THE CRITICAL NATURE of identification in simultaneous equation…

摘要暂缺

摘要暂缺

线性回归中斜率的估计与异常值识别AI 解读

The paper presents a large sample method for estimating the slope parameter in a linear model by minimizing a loss function related to the empirical cumulant generating function of the error distribution. A family of estimators, indexed by a real parameter, is obtained and consistency and asymptotic normality established. The optimum member of the family is that which has minimum variance with respect to the parameter. This minimization together with a characterization…

若干岭估计量的模拟研究AI 解读

Ridge regression, which defines a class of estimators indexed by a biasing parameter k, is an alternative to least squares estimation in the multiple linear regression model. Many algorithms for the biasing parameter have been proposed in the statistical literature. The present study identifies 10 promising algorithms and systematically evaluates and compares them using Monte Carlo methods. Three algorithms perform well overall. A two-parameter estimator offers potential…

圆形结构模型AI 解读

SUMMARY This paper deals with consistent estimation of the structural parameters of the circular structural model. Identifiability is proved and the difficulties of dealing with the structural model in practice mentioned. A uniform distribution is then assumed for the angular variables and maximum likelihood estimation of the parameters examined. Also discussed are use of an approximation to the ratio of modified Bessel functions, asymptotic behaviour of the estimators as…

线性回归中的斜率估计与异常值识别AI 解读

The paper presents a large sample method for estimating the slope parameter in a linear model by minimizing a loss function related to the empirical cumulant generating function of the error distribution. A family of estimators, indexed by a real parameter, is obtained and consistency and asymptotic normality established. The optimum member of the family is that which has minimum variance with respect to the parameter. This minimization together with a characterization…

构造具有可控特征的回归：探查回归表现的方法AI 解读

Studies of robust regression algorithms have generally relied on Monte Carlo trials and have concentrated on evaluating the algorithm's performance for long-tailed error distributions. This article identifies other aspects of data structure that can influence the performance of robust regression procedures and presents an algorithm for constructing regression problems in which each of these factors can be controlled separately. The method generalizes to multiple regression…

构造具有可控特征的回归问题：考察回归性能的方法AI 解读

Studies of robust regression algorithms have generally relied on Monte Carlo trials and have concentrated on evaluating the algorithm's performance for long-tailed error distributions. This article identifies other aspects of data structure that can influence the performance of robust regression procedures and presents an algorithm for constructing regression problems in which each of these factors can be controlled separately. The method generalizes to multiple regression…

噪声条件下大型总体中的个体识别AI 解读

A new approach is presented to the problem of determining whether an individual (the target) appears in a large file where individuals are identified by measurements subject to error. This approach attaches costs to searching and to missing the individual. It corresponds to testing a simple hypothesis, that the measurements on the target and an element in the library have a given joint distribution, against the alternative that they are independent. Certain measures of…

线性函数关系与联立方程中估计量分布的渐近展开AI 解读

We derive asymptotic expansions of the distributions of the maximum likelihood (ML) estimator and the ordinary least squares (OLS) estimator in a linear functional relationship model as the sample size increases infinitely. These expansions are equivalent to the asymptotic expansions of the distributions of the limited information maximum likelihood (LIML) estimator when the covariance is known to within a proportionality constant and the two-stage least squares (TSLS)…

通过最小分量识别有序二元正态分布AI 解读

For full-rank covariance matrix and other subfamilies of the ordered bivariate normal distribution, the identifiability of parameters by the minimum variate is demonstrated. For the general family, the distribution is not identified. These identifiability questions arise in connection with estimation within certain econometric models, for example, Tobin and Fair-Jaffee.

简单变量测量误差模型的工具变量估计AI 解读

Alternative instrumental variable estimators for the slope in the simple errors-in-variables model are discussed. The maximum likelihood estimator is derived for the model in which the error covariance is known to be zero and for the model in which the error covariance is unknown. Modified maximum likelihood estimators and randomly weighted average estimators similar to those studied by Huntsberger are discussed. The limiting distributions of the estimators are presented and…

简单测量误差模型的工具变量估计AI 解读

Alternative instrumental variable estimators for the slope in the simple errors-in-variables model are discussed. The maximum likelihood estimator is derived for the model in which the error covariance is known to be zero and for the model in which the error covariance is unknown. Modified maximum likelihood estimators and randomly weighted average estimators similar to those studied by Huntsberger are discussed. The limiting distributions of the estimators are presented and…

通过最小值变量识别有序二元正态分布AI 解读

For full-rank covariance matrix and other subfamilies of the ordered bivariate normal distribution, the identifiability of parameters by the minimum variate is demonstrated. For the general family, the distribution is not identified. These identifiability questions arise in connection with estimation within certain econometric models, for example, Tobin and Fair-Jaffee.