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线性潜变量模型中不同最小距离估计量的渐近最优性AI 解读

In the context of linear latent-variable models, and a general type of distribution of the data, the asymptotic optimality of a subvector of minimum-distance estimators whose weight matrix uses only second-order moments is investigated. The asymptotic optimality extends to the whole vector of parameter estimators, if additional restrictions on the third-order moments of the variables are imposed. Results related to the optimality of normal (pseudo) maximum likelihood methods…

广义期望效用理论的预测价值AI 解读

Recent experimental choice studies compare expected utility with competing theories of decision-making under risk. Formal tests used to judge the theories usually count the number of consistent responses, ignoring systematic variation in inconsistent responses. A maximum-likelihood estimation method is developed that extracts more information from the data and enables one to judge the predictive utility--fit and parsimony--of utility theories. Analyses of twenty-three data…

单位根检验准则的比较AI 解读

The ordinary least squares estimator has been widely used for testing the unit-root hypothesis in autoregressive processes. Recently, several new criteria, based on maximum likelihood and weighted symmetric estimators, have been proposed. In this article, the authors describe several different test criteria. Results from Monte Carlo studies that compare the power of the different test criteria indicate that the new tests are more powerful against the stationary alternative.…

递增阶梯电价下的居民用电需求：来自受控实验的证据AI 解读

Block-rate structures are used extensively in public-utility pricing, including residential electricity. Estimating price elasticities in this context is complicated by the resulting nonlinearity of the consumer's budget constraint. This article estimates the demand for electricity under inverted block rates using data from a residential rate experiment. The study provides an opportunity to obtain short-run price elasticities in an experimental setting and to compare…

对《随机波动模型的贝叶斯分析》的评论AI 解读

This article contains comments on “Bayesian Analysis of Stochastic Volatility Models,” by Jacquier, Poison, and Rossi. The Markov-chain Monte Carlo (MCMC) method proposed is compared empirically with a simulated maximum likelihood (SML) method. The MCMC and SML estimators yield very similar results, both when applied to actual data and in a Monte Carlo experiment.

随机波动模型的贝叶斯分析AI 解读

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance…

《随机波动模型的贝叶斯分析》评论AI 解读

This article contains comments on 'Bayesian Analysis of Stochastic Volatility Models,' by Jacquier, Polson, and Rossi. The Markov-chain Monte Carlo (MCMC) method proposed is compared empirically with a simulated maximum likelihood (SML) method. The MCMC and SML estimators yield very similar results, both when applied to actual data and in a Monte Carlo experiment.

平均剩余寿命回归模型中的估计AI 解读

SUMMARY The proportional mean residual life model was originally proposed by Oakes and Dasu. This model can be extended to a regression model with explanatory variables. We consider the problem of estimation of the regression parameter in this general model. We investigate two types of estimator, of which one is based on the maximum likelihood equation of the exponential regression model, and the other is based on the underlying proportional hazards structure of the model…

基于选择性二项信息的估计：如何解读被挑选的击球率AI 解读

Often sports announcers, particularly in baseball, provide the listener with exaggerated information concerning a player's performance. For example, we may be told that Dave Winfield, a popular baseball player, has hit safely in 8 of his last 17 chances (a batting average of .471). This is biased, or selected information, as the “17” was chosen to maximize the reported percentage. We model this as observing a maximum success rate of a Bernoulli process and show how to…

用二元数据测量潜在子群变化：无条件、条件与半参数极大似然估计AI 解读

Changes in dichotomous data caused by treatments can be analyzed by means of the so-called linear logistic model with relaxed assumptions (LLRA). In contrast to models of the Rasch type, the LLRA allows incidental multidimensional parameters describing the response behavior of the subjects so that each observable criterion used for assessing the changes is governed by its own subject parameter. Because generalizability of the treatment effects over subjects and criteria is…

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利用二元数据测量潜在亚组的变化：三类极大似然估计AI 解读

Changes in dichotomous data caused by treatments can be analyzed by means of the so-called linear logistic model with relaxed assumptions (LLRA). In contrast to models of the Rasch type, the LLRA allows incidental multidimensional parameters describing the response behavior of the subjects so that each observable criterion used for assessing the changes is governed by its own subject parameter. Because generalizability of the treatment effects over subjects and criteria is…

选择性二项信息下的估计：如何理解被挑选的棒球打击率AI 解读

Often sports announcers, particularly in baseball, provide the listener with exaggerated information concerning a player's performance. For example, we may be told that Dave Winfield, a popular baseball player, has hit safely in 8 of his last 17 chances (a batting average of .471). This is biased, or selected information, as the “17” was chosen to maximize the reported percentage. We model this as observing a maximum success rate of a Bernoulli process and show how to…

摘要暂缺

异方差回归模型中非正则似然函数的一则注记AI 解读

The existence of maximum likelihood estimates for a class of heteroscedastic regression models is considered. For a given dispersion function we show that, under a weak condition, the likelihood is singular at points corresponding to nonreplicated observations, causing unrestricted maximum likelihood estimation to break down, whilst for an alternative class of dispersion functions we obtain a much stronger linear independence condition for the likelihood to be unbounded.

To facilitate maximum likelihood estimation for Box-Cox models, several authors have suggested dividing the dependent variable by its sample geometric mean. This paper points out previously unmentioned drawbacks of this 'recalling.' First, the 'resealed' model is not actually equivalent to the untransformed one, so that the procedure involves more than a unit change. Second, there is no clear interpretation of the parameters after such resealing. The authors suggest an…

鸟类环志回收模型中的边界估计AI 解读

SUMMARY A simple model for avian survival often leads to boundary estimates when the model is fitted to data by maximum likelihood. Data may result from ring recovery experiments alone, or in combination with a radio tracking investigation. A study of profile likelihoods enables the conditions under which boundary estimates occur to be completely specified. Fitting an incorrect model is shown to result in inconsistent estimators of all parameters, and an asymptotic certainty…

通过 EM 算法获得的最大惩罚似然估计的方差AI 解读

SUMMARY We address the problem of providing variances for parameter estimates obtained under a penalized likelihood formulation through use of the EM algorithm. The solution proposed represents a synthesis of two existent techniques. Firstly, we exploit the supplemented EM algorithm developed by Meng and Rubin that provides variance estimates for maximum likelihood estimates obtained via the EM algorithm. Their procedure relies on evaluating the Jacobian of the mapping…

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单变量季节时间序列建模的多变量方法AI 解读

A seasonal time series can be represented by a vector autoregressive model for the annual series containing the seasonal observations. This model allows for periodically varying coefficients. When the vector elements are integrated, the maximum likelihood cointegration method can be used to check for the presence of, possibly restricted, cointegration relations between these annual series. In this paper it is shown that this application generalizes a test procedure for…

将潜在产出作为潜变量进行估计AI 解读

This article proposes a new method for estimating potential output in which potential real gross domestic product (GDP) is modeled as an unobserved stochastic trend and deviations of GDP from potential affect inflation through an aggregate supply relationship. The output and inflation equations together form a bivariate unobserved-components model that is estimated via maximum likelihood through the use of the Kalman-filter algorithm. The procedure yields a measure of…

带移入的临界分枝过程中 Bootstrap 的无效性AI 解读

This article considers a case of parametric bootstrap when the observations consist of generation sizes of the branching process with immigration together with the immigration component of each generation. Suppose we estimate the offspring mean $m$ by the maximum likelihood estimator (m.l.e.). It is then shown that the bootstrap version of the standardized m.l.e. does not have the same limiting distribution as the standardized m.l.e., under the assumption that $m = 1$ (critic…

最大似然估计与后验分布非正态性的若干诊断AI 解读

Standard large-sample maximum likelihood and Bayesian inference, based on limiting multivariate normal distributions, may be dubious when applied with small or moderate sample sizes. We define and discuss several measures of nonnormality of MLE and posterior distributions that may be used as diagnostics and can indicate whether reparameterization will be effective in improving inferences. We begin by showing how the nonlinearity measures introduced by Beale and Bates and…

筛估计的收敛速度AI 解读

In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates (MLE's) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces. In many cases, especially when the parameter space is infinite dimensional, maximization over the whole parameter space is undesirable. In such cases, one has to perform maximization over an approximating space (sieve) of the original parameter space…