日期精度：到日 1310 篇 · 到月 1919 篇 · 到年 331 篇 · 未提供 1 篇
小扰动情形下有限信息最大似然估计量的改进
The limited information maximum likelihood (LIML) estimator is shown to be inadmissible in terms of asymptotic expansions of the distributions of the estimators. The LIML estimator is improved by combining it linearly with the two-stage or the ordinary least squares estimator.
方差分量的非负估计量与预检验估计量
Nonnegative estimators of variance components involving preliminary tests are studied. The restricted maximum likelihood estimator and other truncated estimators may be viewed as preliminary test estimators. A new preliminary test estimator is proposed and studied. Recommendations of the levels of preliminary tests are made.
若干已知方差多元正态模型中相关系数的简便最优渐近正态估计
It is well-known that in certain multivariate normal models with known variances, maximum likelihood estimation of correlations is arithmetically cumbersome. For certain of these models, a relatively simple estimation technique based on the sufficient statistics is given and shown to be best asymptotically normal. The procedure uses the fact that many of the likelihood equations for these models are essentially cubic equations. A consistent, explicit solution for the…
最大似然估计精度的评估：观测与期望Fisher信息
This paper concerns normal approximations to the distribution of the maximum likelihood estimator in one-parameter families. The traditional variance approximation is 1/§, where θ is the maximum likelihood estimator and § is the expected total Fisher information. Many writers, including R. A. Fisher, have argued in favour of the variance estimate 1/I(x), where I(x) is the observed information, i.e. minus the second derivative of the log likelihood function at θ given data x.…
估计不确定性下的决策
This paper provides necessary and sufficient conditions for it to be optimal to base decisions on estimates of the parameters that characterize a decision problem (e.g., profit maximization with an estimated price elasticity of demand). We show that the separation of parameter estimation from decision making generally yields lower utility than an integrated approach which takes account of estimation uncertainty. We evaluate the decision in the parameter estimation method and…
良好与最优的岭估计量
In generalized ridge estimation, the components of the ordinary least squares (OLS) regression coefficient vector which lie along the principal axes of the given regressor data are rescaled using known ridge factors. Generalizing a result of Swindel and Chapman, it is shown that, if each ridge factor is nonstochastic, nonnegative, and less than one, then there is at most one unknown direction in regression coefficient space along which ridge coefficients have larger mean…
严格递增回归函数的估计
The maximum likelihood estimate of a nondecreasing regression function with independent, normal errors has been studied in detail in the literature. However, it is often constant over one or more intervals and for this reason may be unacceptable if the regression function is believed to be strictly increasing. Using simulation techniques, the least squares line, the maximum likelihood estimate, and a weighted average of these two are compared here. In each case considered,…
线性超结构模型的广义最小二乘估计
Generalized least squares estimation is considered for the structural model when there are several groups with disparate locations. When variances are homogeneous over groups the solution is shown to be identical with the maximum likelihood solution for some parameters and asymptotically equivalent for the remainder
Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The…
分类极大似然估计的渐近行为
This paper examines maximum likelihood techniques as applied to classification and clustering problems, and shows that the classification maximum likelihood technique, in which individual observations are assigned on an “all-or-nothing” basis to one of several classes as part of the maximization process, gives results which are asymptotically biased. This extends Marriott’ls (1975) work for normal component distributions. Numerical examples are presented for normal component…
尼罗河问题：变点问题的条件解
Inference is considered for the point in a sequence of random variables at which the probability distribution changes. An approximation to the conditional distribution of the maximum likelihood estimator of the changepoint given the ancillary values of observations adjacent to the estimated changepoint is derived and shown to be numerically equal to a Bayesian posterior distribution for the changepoint. A hydrological example is given to show that inferences based on the…
位置与尺度参数的似然推断
We obtain a normal approximation to the conditional distribution of maximum likelihood estimates in location-scale families. The covariance matrix is the inverse of minus the observed log likelihood second derivative matrix at the likelihood maximum, as opposed to the inverse of Fisher information, to which it is superior. Confidence intervals for the location parameter are discussed. Small sample numerical results are given for the Cauchy case to illustrate the accuracy of…
部分删失数据的半马尔可夫模型
Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The…
位置与尺度参数的似然推断
We obtain a normal approximation to the conditional distribution of maximum likelihood estimates in location-scale families. The covariance matrix is the inverse of minus the observed log likelihood second derivative matrix at the likelihood maximum, as opposed to the inverse of Fisher information, to which it is superior. Confidence intervals for the location parameter are discussed. Small sample numerical results are given for the Cauchy case to illustrate the accuracy of…
线性超结构模型中的广义最小二乘
Generalized least squares estimation is considered for the structural model when there are several groups with disparate locations. When variances are homogeneous over groups the solution is shown to be identical with the maximum likelihood solution for some parameters and asymptotically equivalent for the remainder
基于 Gini 统计量的指数分布尺度无关拟合优度检验
Summary The sample Gini index is shown to be a powerful scale-free test of exponentiality against a variety of alternatives. Compared to the maximum likelihood test, the asymptotic relative efficiency of the Gini statistic is 0·69 against gamma and 0·88 against Weibull alternatives. Exact and asymptotic distribution theory is developed. On the basis of good power compared to competing tests, ease of computation, availability of exact critical values and robustness to…
James–Stein 估计量的风险估计最优性
This note extends a result of Efron and Morris on domination of the maximum likelihood estimator for the mean of a multivariate normal distribution. We show that this result and our extension follow from a certain differential inequality. In a certain class of estimators having a unique unbiased estimator for the quadratic risk we find necessary and sufficient conditions for risk estimate dominance of a particular set of estimators. We show that, in the sense of risk…
渐近有效的递归估计
Stochastic approximation procedures were shown by Sakrison to become asymptotically efficient estimators when used to minimize the Kullback-Leibler information, if certain conditions hold. Further results in this direction were obtained by Nevel'son and Has'minskij. This paper gives, first, alternative conditions for convergence and, secondly, shows that, under weaker conditions, asymptotic optimality is obtained by a modified stochastic approximation procedure. The modified…
最大似然估计的刀切法
This paper proves the apparently outstanding conjecture that the maximum likelihood estimate (m.l.e.) "behaves properly" when jackknifed. In particular, under the usual Cramer conditions (1) the jackknifed version of the consistent root of the m.l. equation has the same asymptotic distribution as the consistent root itself, and (2) the jackknife estimate of the variance of the asymptotic distribution of the consistent root is itself consistent. Further, if the hypotheses of…
简单变量测量误差模型中最小二乘与工具变量估计的比较
IF ONE OF THE EXPLANATORY VARIABLES in a linear regression model is measured with error, the ordinary least squares estimator is known to be biased and inconsistent. Given suitable assumptions, an instrumental variables estimator is known to be consistent. In a large sample, the instrumental variables estimator is thus unambiguously preferred, but the choice of an estimator in a small sample remains a puzzle. The method of maximum likelihood sheds light on this puzzle. It…
联立方程系统中的内生虚拟变量
This paper considers the formulation and estimation of simultaneous equation models with both discrete and continuous endogenous variables.The statistical model proposed here is sufficiently rich to encompass the álassjcai simultaneous equation model for continuous endogenous variables and more recent models for purely discrete endogenous variables as special cases of a more general model.Interest in discrete data has been ftsledby a rapid growth in the availability of…
非线性估计与渐近近似
central objective of this paper is to present a series expansion of nonlinear estimators in order to facilitate an analysis of the distributions of such estimators. Where the estimator under consideration is a maximum likelihood estimator, the method provides somewhat more information, as well as higher order approximations to the distributions of the nonlinear estimators than does the usual theory which demonstrates asymptotic normality. The method is also useful for a wide…