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信息矩阵奇异时随机前沿模型最大似然估计的渐近分布AI 解读

This paper investigates the asymptotic distribution of the maximum likelihood estimator in a stochastic frontier function when the firms are all technically efficient. For such a situation the true parameter vector is on the boundary of the parameter space, and the scores are linearly dependent. The asymptotic distribution of the maximum likelihood estimator is shown to be a mixture of certain truncated distributions. The maximum likelihood estimates for different parameters…

MA(1) 与局部水平模型最大似然估计量的分布AI 解读

Although considerable attention has recently been paid to the behavior of the maximum likelihood estimator of simple moving average models, little progress has been made in finding a good approximation to its distribution in cases where the process is close to being noninvertible. In this paper a method is produced that gives an excellent approximation to the distribution function, even in the case where the process is strictly noninvertible. Also studied is the related…

摘要暂缺

单整过程的连续弱收敛与随机等度连续性：回归估计应用AI 解读

The concepts of continuous and uniform weak convergence and versions of stochastic equicontinuity are discussed in the context of integrated processes of order one. The considered processes depend on a parameter vector in a specific fashion which is relevant for integrated and cointegrated systems with non-linearities in parameters. The results of the paper can be applied to obtain asymptotic distributions of estimators and test statistics in such systems. In a correctly…

利用面板数据识别和估计含外生变量的连续时间动态系统AI 解读

This paper deals with the identification and maximum likelihood estimation of the parameters of a stochastic differential equation from discrete time sampling. Score function and maximum likelihood equations are derived explicitly. The stochastic differential equation system is extended to allow for random effects and the analysis of panel data. In addition, we investigate the identifiability of the continuous time parameters, in particular the impact of the inclusion of…

配置系统中理论约束与自相关的联合检验AI 解读

Abstract In the context of allocation models with vector autoregressive errors we propose a convenient procedure, based on the Lagrange multiplier principle, for testing any possible combination of absence of serial correlation, homogeneity, and symmetry against any possible alternative which specifies autocorrelation of an arbitrary given order. We also derive generic expressions for the maximum likelihood estimation of the models under six possible combinations of…

双参数指数分布族的序贯估计结果AI 解读

We consider the problem of sequentially estimating one parameter in a class of two-parameter exponential family of distributions. We assume a weighted squared error loss with a fixed cost of estimation error. The stopping rule, based on the maximum likelihood estimate of the nuisance parameter, is shown to be independent of the terminal estimate. The asymptotic normality of the stopping variable is established and approximations to its mean and to the regret associated with…

回归函数可加性的检验AI 解读

Observations $y_{ij}$ are made at points $(x_{1i},x_{2j})$ according to the model $y_{iy}=F(x_{1i},x_{2j})+e_{ij}$, where the $e_{ij}$ are independent normals with constant variance. In order to test that $F(x_1,x_2)$ is an additive function of $x_1$ and $x_2$, a likelihood ratio test is constructed comparing $F(x_1,x_2)=\mu+Z_1 (x_1)+Z_2(x_2)$ with $F(x_1,x_2)=\mu+Z_1(x_1)+Z_2(x_2)+Z(x_1,x_2)$, where $Z_1$, $Z_2$ are Brownian motions and Z is a Brownian sheet. The ratio of…

截断数据乘积极限估计量的几乎必然表示AI 解读

In the left-truncation model, one observes data $(X_i,Y_i)$ only when $Y_i\leq X_i$. Let F denote the marginal d.f. of $X_i$ , the variable of interest. The nonparametric MLE $\hat{F}_n$ of F aims at reconstructing F from truncated data. In this paper an almost sure representation of $\hat{F}_n$ is derived with improved error bounds on the one hand and under weaker distributional assumptions on the other hand.

若干非参数最大似然估计量的 Hellinger 一致性AI 解读

Consider a class $\mathscr{P}={P_\theta:\theta\in\Theta}$ of probability measures on a measurable space $(\mathscr{X},\mathscr{A})$, dominated by a $\sigma$ -finite measure $\mu$. Let $f_\theta=dP_\theta/d_\mu$, $\theta\ in\Theta$, and let $\theta_n$ be a maximum likelihood estimator based on n independent observations from $P_{\theta_0}$, $\theta_0\in\Theta$. We use results from empirical process theory to obtain convergence for the Hellinger distance $h(f_{\hat{\theta}_n}, f_{\theta_0})$…

稳健估计、非正态性与广义指数分布AI 解读

The detection and treatment of outliers in data has represented an important area of research. The general approach that has been adopted involves observing that outliers contribute to the “fatness” in the tails of the error distribution and using an appropriate model of the error term to take this into account. This article introduces a general dass of distributions to capture nonnormalities in the data based on the generalized exponential family. This family of…

利用共轭梯度加速 EM 算法AI 解读

The EM algorithm is a very popular and widely applicable algorithm for the computation of maximum likelihood estimates. Although its implementation is generally simple, the EM algorithm often exhibits slow convergence and is costly in some areas of application. Past attempts to accelerate the EM algorithm have most commonly been based on some form of Aitken acceleration. Here we propose an alternative method based on conjugate gradients. The key, as we show, is that the EM…

椭圆总体中基于伪最大似然估计的统计推断AI 解读

In this article we develop statistical inference based on the maximum likelihood method in elliptical populations with an unknown density function. The method assuming the multivariate normal distribution, using the sample mean and the sample covariance matrix, is basically correct even for elliptical populations under a certain kurtosis adjustment, but is not statistically efficient, especially when the kurtosis of the population distribution has higher than moderate…

多变量不完整数据的模式混合模型AI 解读

Consider a random sample on variables X1, …, Xv with some values of Xv missing. Selection models specify the distribution of X1 , …, XV over respondents and nonrespondents to Xv , and the conditional distribution that Xv is missing given X1 , …, Xv . In contrast, pattern-mixture models specify the conditional distribution of X 1, …, Xv given that XV is observed or missing respectively and the marginal distribution of the binary indicator for whether or not Xv is missing. For…

时间序列模型参数与异常值效应的联合估计AI 解读

Time series data are often subject to uncontrolled or unexpected interventions, from which various types of outlying observations are produced. Outliers in time series, depending on their nature, may have a moderate to significant impact on the effectiveness of the Standard methodology for time series analysis with respect to model identification, estimation, and forecasting. In this article we use an iterative outlier detection and adjustment procedure to obtain joint…

长序列一般线性过程似然重复计算的快速算法AI 解读

The likelihood function of a stationary and invertible autoregressive moving average (ARMA) process for a given series is a function of the determinant of the covariance matrix of the series and a quadratic form involving the inverse of this matrix. In this article the quadratic form is transformed into a new quadratic form, whose matrix is evaluated readily and has constant elements along its diagonals; that is, it is a Symmetrie Toeplitz matrix. It allows the computation…

广义线性混合模型中的近似推断AI 解读

Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior…

纵向二元响应的似然分析方法AI 解读

In this paper, we discuss a likelihood-based method for analysing correlated binary responses based on a multivariate model. It is related to the pseudo-maximum likelihood approach suggested recently by Zhao & Prentice (1990). Their parameterization results in a simple pairwise model, in which the association between responses is modelled in terms of correlations, while the present paper uses conditional log odds-ratios. With this approach, higher-order associations can be…

从置信区间进行贝叶斯与似然计算AI 解读

Recently there has been considerable progress on setting good approximate confidence intervals for a single parameter θ in a multi-parameter family. Here we use these frequentist results as a convenient device for making Bayes, empirical Bayes and likelihood inferences about θ. A simple formula is given that produces an approximate likelihood function Lx†(θ) for θ, with all nuisance parameters eliminated, based on any system of approximate confidence intervals. The…

结合先验信息的非参数样条回归AI 解读

By using prior information about the regression curve we propose new nonparametric regression estimates. We incorporate two types of information. First, we suppose that the regression curve is similar in shape to a family of parametric curves characterized as the solution to a linear differential equation. The regression curve is estimated by penalized least squares with the differential operator defining the smoothness penalty. We discuss in particular growth and decay…

二元响应模型的有效半参数估计量AI 解读

This paper proposes an estimator for discrete choice models that makes no assumption concerning the functional form of th e choice probability function where this function can be characterized by an index. The estimator is shown to be consistent, asymptotically normally distributed, and to achieve the semiparametric efficiency bound. Monte Carlo evidence indicates that there may be only modest efficiency losses relative to maximum likelihood estimation when the distribution…

方差异质性建模：残差最大似然与诊断AI 解读

SUMMARY The assumption of equal variance in the normal regression model is not always appropriate. to attempt to eliminate unequal variance a transformation is often used but if the transformation is not successful, or the variances are of intrinsic interest, it may be necessary to model the variances in some way. We consider the normal regression model when log-linear dependence of the variances on explanatory variables is suspected. Detection of the dependence, estimation…

二元配对二值数据的逻辑回归参数估计AI 解读

SUMMARY Consider bivariate binary data, with possibly different covariates for each marginal binary observation. Suppose that the correlation between paired observations is a nuisance, and the interest lies in estimating logistic regression parameters associated with the covariates. We consider the estimator obtained by using the independence estimating equation, with corrected variance. This estimator can be very efficient and robust compared with maximum likelihood or the…