A survey of statistical methods on remote sensing in russian literature from 1976 to 1985

In this article we review the major areas of remote sensing in the Russian literature for the period 1976 to 1985 that use statistical methods to analyze the observed data. For each of the areas, the problems that have been studied and the statistical techniques that have been used are briefly described     

Risk minimization for an insurer with investment and reinsurance via g-expectation

Abstract

This paper is devoted to the study of a risk-based optimal investment and proportional reinsurance problem. The surplus process of the insurer and the risky asset process in the financial market are assumed to be general jump-diffusion processes. We use a convex risk measure generated by g-expectation to describe the risk of the terminal wealth with investment and reinsurance. Under the aim of minimizing the risk, the problem is solved by using techniques of stochastic maximum principles. Two interesting special cases are studied and the explicit expressions for optimal strategies and corresponding minimal risks are derived.     

SOME SECOND ORDER ASYMPTOTICS IN EXPONENTIAL FAMILY NON-LINEAR REGRESSION MODELS (A GEOMETRIC APPROACH)

The differential geometric framework of Amari (1982a, 1985) is applied to the study of some second order asymptotics related to the curvatures for exponential family nonlinear regression models, in which the observations are independent but not necessarily identically distributed. This paper presents a set of reasonable regularity conditions which are needed to study asymptotics from a geometric point of view in regression models. A new stochastic expansion of a first order efficient estimator is derived and used to study several asymptotic problems related to Fisher information in terms of curvatures. The bias and the covariance of the first order efficient estimator are also calculated according to the expansion.     

正态分布随机Petri网的串并行结构化简

基于正态分布随机Petri网的串行和并行两种基本结构的化简开展研究,推导出在正态分布广义随机Petri网中化简串行模型的等价理论公式,即合成执行时间为各个变迁的执行时间之和,仍服从正态分布,并且合成均值等于所有变迁执行时间的均值之和,合成标准偏差等于所有变迁执行时间标准偏差的方和根。还提出了并行模型的模拟仿真计算方法,分析了两个并行变迁独立不同分布时的情况,并给出了近似估算公式,在公式中,均值较大的变迁权重较大。     

基于随机前沿方法的我国房地产上市公司X-效率分析

采用随机前沿方法，以色诺芬数据库中2000-2006年间34家中国房地产上市公司作为模型估计样本对我国房地产上市公司的X-效率进行研究，试图考察我国房地产上市公司的效率变迁状况。研究结果表明，我国房地产上市公司的经营绩效是逐年上升的，但5年平均的X-效率是0．599，我国房地产行业整体绩效依然偏低，其效率的提升空间非常大。     

A stochastic approximation algorithm for maximum-likelihood estimation with incomplete data

We propose a new stochastic approximation (SA) algorithm for maximum-likelihood estimation (MLE) in the incomplete-data setting. This algorithm is most useful for problems when the EM algorithm is not possible due to an intractable E-step or M-step. Compared to other algorithm that have been proposed for intractable EM problems, such as the MCEM algorithm of Wei and Tanner (1990), our proposed algorithm appears more generally applicable and efficient. The approach we adopt is inspired by the Robbins-Monro (1951) stochastic approximation procedure, and we show that the proposed algorithm can be used to solve some of the long-standing problems in computing an MLE with incomplete data. We prove that in general O(n) simulation steps are required in computing the MLE with the SA algorithm and O(n log n) simulation steps are required in computing the MLE using the MCEM and/or the MCNR algorithm, where n is the sample size of the observations. Examples include computing the MLE in the nonlinear error-in-variable model and nonlinear regression model with random effects.     

基于行程时间可靠性的随机交通网络均衡模型

考虑现实条件下交通网络的不确定性状态，提出了从确定型网络到不确定型网络状态下出行者路径选择行为的改变，及基于行程时间可靠性的路径选择模型。并将可靠行程时间的路径选择行为纳入到随机交通网络平衡分配模型中，证明了模型的等价性和唯一性。在一个小型测试网络上对模型分配结果进行了测试，测试结果与经验相吻合，表明模型具有较好的现实可行性。     

实用Box-Jenkins预测模型及应用

介绍 Box-Jenkins 的实用预测模型,并提出用随机模拟法模型的参数进行估计。与通常使用的最小二乘法相比,模拟法无需考虑初始值的选取,就能保证迭代过程的收敛性。整个程序的编制使用模块化结构,使预测者无需了解太多的这方面知识,就能方便地使用。在案例的预测过程中,只需进行几次人工干预,就能输出多种分析结果。     

一种非周期软实时系统的缓冲设计方法

研究了任务的到达时间和执行时间均不确定的非周期软实时系统,采用排队论的分析方法,非周期软实时系统被看成是典型的随机离散事件动态系统,并被模型化为一个M/M/1/H混合制排队系统,讨论了抛弃率、截止时间错过率和平均截止时间超过量等三种性能指标,并在此基础上进行了缓冲容量的最优设计。性能分析表明缓冲设计方法能有效地改善系统的性能。     

Joint Initial Stocking and Transshipment—Asymptotics and Bounds

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply‐demand mismatch somewhere, a re‐distribution or transshipment will be needed. Hence, there are two decisions involved: the one‐time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two‐location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square‐root‐of T. Hence, in the context of high‐volume, fast‐moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.