Multivariate Exponentially Weighted Moving Average Charts for a Mean Based on Its Prediction Distribution

This article develops a control chart for a mean vector when it is monitored by a quadratic form in the exponentially weighted observation vector. A Bayesian approach is used to incorporate parameter uncertainty. We first use a Bayesian predictive distribution to construct the control chart, and we then use a sampling theory approach to evaluate it under various hypothetical specifications for the data generation model.     

Linear Estimators and Predictors Based on Generalized Order Statistics from Generalized Pareto Distributions

Linear estimation and prediction based on several samples of generalized order statistics from generalized Pareto distributions is considered. Representations of best linear unbiased estimators (BLUEs) and best linear equivariant estimators in location-scale families are derived, as well as corresponding optimal linear predictors. Moreover, we study positivity of the linear estimators of the scale parameter. An example illustrates that the BLUE may attain negative values with positive probability in certain situations.     

Combined Exponentially Weighted Moving Average Charts for the Mean and Variance Based on the Predictive Distribution

This article develops combined exponentially weighted moving average (EWMA) charts for the mean and variance of a normal distribution. A Bayesian approach is used to incorporate parameter uncertainty. We first use a Bayesian predictive distribution to construct the control chart, and we then use a sampling theory approach to evaluate it under various hypothetical specifications for the data generation model. Simulations are used to compare the proposed charts for different values of both the weighing constant for the exponentially weighted moving averages and for the size of the calibration sample that is used to estimate the in-statistical-control process parameters. We also examine the separate performance of the EWMA chart for the variance.     

The effect of parameter estimation on the performance of one-sided Shewhart control charts for zero-inflated processes

ABSTRACT

Zero-inflated probability models are used to model count data that have an excessive number of zeros. Shewhart-type control charts have been proposed for the monitoring of zero-inflated processes. Usually their performance is evaluated under the assumption of known process parameters. However, in practice, their values are rarely known and they have to be estimated from an in-control historical Phase I sample. In the present paper, we investigate the performance of Shewhart-type control charts for zero-inflated processes with estimated parameters and propose practical guidelines for the statistical design of the examined charts, when the size of the preliminary sample is predetermined.     

Simultaneous estimation of λs for poisson distributions

Consider the problem of simultaneously estimating an integral power of the parameters of POISSOK populations using independent samples. Let the loss be the sum of quadratic losses for the components. An estimator which is better than the unbiased esti¬mator is obtained     

On nonlinear models for time series

The paper is a review of nonlinear processes used in time series analysis and presents some new original results about stationary distribution of a nonlinear autoregres-sive process of the first order. The following models are considered: nonlinear autoregessive processes, threshold AR processes, threshold MA processes, bilinear models, auto-regressive models with random parameters including double stochastic models, exponential AR models, generalized threshold models and smooth transition autoregressive models, Some tests for linearity of processes are also presented.     

Tests of significance for the odds ratio in a 2×2 table based on the ratio of two independent F-variates

Following Gart (1966) a test of significance for the odds ratio in a 2×2 table is developed based on a semi-empirical method of approximating discrete distributions by their continuous analogues. The distribution of the test statistic (W), the ratio of two independent F-variates, is derived. This approximate technique is compared with the "exact" test, uncorrected X test, and a normal approximation based on lnW.     

地层倾角测井沉积构造与层序界面识别

采用相关对比法对关键井的地层倾角测井资料进行处理,结合地质构造图反映出的构造倾向,确定倾角资料长相关处理参数;结合岩芯照片对倾角资料进行微细处理,确定短相关处理参数。利用确定的长、短相关处理参数,对哈得逊地区的倾角测井资料进行处理,建立了东河砂岩储层典型的层理和微构造倾角解释模型;对中泥岩段开展了层序地层研究,根据长窗长倾角矢量图和累积倾角图,在中泥岩段识别出1个完整的三级层序、2个准层序组和5个准层序;利用短窗长处理的倾角矢量图,结合原始电导率曲线和倾角测井解释结果,进一步识别出中泥岩段有7个岩层组和16个岩层。     

基于精算模型参数调整的农村养老金可持续性仿真研究

随着人口老龄化进程加剧和新型农村社会养老保险制度全面覆盖,农村老年人群与养老需求激增,农村养老金收支失衡与不可持续风险快速加大。本文基于新农保筹资和给付的双重视角,应用总体法构建农村养老金收支与平衡精算模型,基于全国东、中、西部三省六县(市、区)新农保试点地区实地调研数据(有效问卷5031份),通过甄选并调整关键参数,预测农村养老金收支及差额变化与发展趋势,对其可持续性进行仿真研究,针对预测结果提出增强农村养老金可持续性的改革思路。研究结果表明,政策参数缴费率、补贴率和经济参数农民收入增长率、基金投资收益率的提高及其联动增长均有利于增强农村养老金的可持续性,而养老金计发月数的增减对农村养老金可持续性的影响方向并不统一,二者的联合增长对农村养老金的长期可持续性发展具有积极作用。本研究对于丰富、发展农村养老金精算建模与仿真理论、方法及农村社会保障制度完善、政策改进具有重要的理论意义和实践价值。     

无偏GM(1,1)幂模型其及应用

基于GM(1,1)幂模型的模拟误差分析,本文提出了无偏GM(1,1)幂模型及其参数优化方法.从理论上证明了无偏GM(1,1)幂模型对传统GM(1,1)幂模型及其本身的时间响应函数所表达的曲线进行模拟和预测具有重合性,其参数优化方法可以准确识别原始数据所蕴含的参数特性,完全消除了GM(1,1)幂模型自身固有的偏差.其建模过程避免了传统方法由差分方程向微分方程的跳跃导致的误差,应用范围覆盖了无偏GM(1,1)模型和离散灰色模型.数值模拟和实例分析表明,无偏GM(1,1)幂模型使得传统模型的模拟与预测精度得到了显著的改善.