Entropic Latent Variable Integration via Simulation

This paper introduces a general method to convert a model defined by moment conditions that involve both observed and unobserved variables into equivalent moment conditions that involve only observable variables. This task can be accomplished without introducing infinite‐dimensional nuisance parameters using a least favorable entropy‐maximizing distribution. We demonstrate, through examples and simulations, that this approach covers a wide class of latent variables models, including some game‐theoretic models and models with limited dependent variables, interval‐valued data, errors‐in‐variables, or combinations thereof. Both point‐ and set‐identified models are transparently covered. In the latter case, the method also complements the recent literature on generic set‐inference methods by providing the moment conditions needed to construct a generalized method of moments‐type objective function for a wide class of models. Extensions of the method that cover conditional moments, independence restrictions, and some state‐space models are also given.     

混合威布尔分布参数估计的L-M算法

混合威布尔分布模型常用来分析具有多种失效模式的机械系统或零部件的可靠性寿命数据,为提高混合威布尔分布未知参数估计的精度,利用非线性最小二乘理论,建立了小子样条件下两重混合威布尔分布参数优化估计模型,将L-M算法用于优化求解。以概率图参数估计法的结果作为迭代初始值,提高了迭代求解的速度。计算实例表明利用该方法估计混合威布尔分布参数是可行的,而且能够获得较精确的结果。     

Relative efficiency of first difference estimator in panel data regression with serially correlated error components

We consider the pooled cross-sectional and time series regression model when the disturbances follow a serially correlated one-way error components. In this context we discovered that the first difference estimator for the regression coefficients is equivalent to the generalized least squares estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a first order autoregressive process where the autocorrelation is close to unity.     

Convergence rates of change-point estimators and tail probabilities of the first-passage-time process

In the classical setting of the change-point problem, the maximum-likelihood estimator and the traditional confidence region for the change-point parameter are considered. It is shown that the probability of the correct decision, the coverage probability and the expected size of the confidence set converge exponentially fast as the sample size increases to infinity. For this purpose, the tail probabilities of the first passage times are studied. General inequalities are established, and exact asymptotics are obtained for the case of Bernoulli distributions. A closed asymptotic form for the expected size of the confidence set is derived for this case via the conditional distribution of the first passage times.     

Perturbation analysis for an orthogonal least-squares estimator

Coordinate measuring machines (CMMs) are used to check the geometric integrity of component parts. The geometric constraints to which a part must conform, as defined e.g., by The American National Standards Institute, assume the use of some type of gauging system when inspecting the part. Statistical issues arise in interpretting CMM data in the inspection of part tolerances. We consider a set of n planar regions on the surface of a part. The unit vector normal to each plane is estimated by orthogonal least squares. The small-sample density of this estimator (on the unit sphere S²) is determined asymptotically as the variance of the CMM error approaches 0. To a first-degree approximation, this density is Fisher-von Mises. Diagnostics are reviewed to test the geometric constraint that the n planar regions are oriented correctly with respect to one another, and to test the flatness of planar regions.     

普请与参禅——论马祖洪州禅"作用即性"的生活实践

中唐马祖道一为代表的洪州禅提倡的“作用即性”的思想,一方面因其将“弹指动目”等行为视为“佛性”的体现而受到石头禅系的批评;另一方面在肯定日用生活的神圣性之时,解释了洪州禅乃至整个南宗禅劳动实践的佛学意义。百丈怀海建立禅门规式,其中的“普请法”便是“作用即性”思想的产物。在普请的劳动实践中完成禅理的参究,是洪州禅的一大特色,并影响到禅门其他宗派。     

Likelihood Ratio Confidence Bands in Non–parametric Regression with Censored Data

Let ( X , Y ) be a random vector, where Y denotes the variable of interest possibly subject to random right censoring, and X is a covariate. We construct confidence intervals and bands for the conditional survival and quantile function of Y given X using a non-parametric likelihood ratio approach. This approach was introduced by Thomas & Grunkemeier (1975 ), who estimated confidence intervals of survival probabilities based on right censored data. The method is appealing for several reasons: it always produces intervals inside [0, 1], it does not involve variance estimation, and can produce asymmetric intervals. Asymptotic results for the confidence intervals and bands are obtained, as well as simulation results, in which the performance of the likelihood ratio intervals and bands is compared with that of the normal approximation method. We also propose a bandwidth selection procedure based on the bootstrap and apply the technique on a real data set.     

Least absolute deviations estimation via the EM algorithm

This paper derives EM and generalized EM (GEM) algorithms for calculating least absolute deviations (LAD) estimates of the parameters of linear and nonlinear regression models. It shows that Schlossmacher's iterative reweighted least squares algorithm for calculating LAD estimates (E.J. Schlossmacher, Journal of the American Statistical Association 68: 857–859, 1973) is an EM algorithm. A GEM algorithm for computing LAD estimates of the parameters of nonlinear regression models is also provided and is applied in some examples.     

A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum ϕ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure ϕ_(λ) Research partially done when was visiting the Bowling Green State University as the Distinguished Lukacs Professor     

Simulation-based inference for simultaneous processes on regular lattices

The article proposes a simulation-based inferential method for simultaneous processes defined on a regular lattice. The focus is on spatio-temporal processes with a simultaneous component, that is such that contemporaneous spatial neighbors are potential explanatory variables in the model. The new method has the advantage of being simpler to implement than maximum likelihood and allows us to propose a robust estimator. We give asymptotic properties, present a Monte Carlo study and an illustrative example.