Semiparametric Efficient Estimation of the Mean of a Time Series in the Presence of Conditional Heterogeneity of Unknown Form

We obtain semiparametric efficiency bounds for estimation of a location parameter in a time series model where the innovations are stationary and ergodic conditionally symmetric martingale differences but otherwise possess general dependence and distributions of unknown form. We then describe an iterative estimator that achieves this bound when the conditional density functions of the sample are known. Finally, we develop a “semi-adaptive” estimator that achieves the bound when these densities are unknown by the investigator. This estimator employs nonparametric kernel estimates of the densities. Monte Carlo results are reported.     

Fuzzy区间值函数项级数及其一致收敛性

文章在已知Fuzzy函数项级数一致收敛概念的基础上,补充了区间值函数项级数一致收敛的概念和判别方法,给出了一致收敛性的区间值函数项级数的分析性质。     

机械基础系列课程教改中机械精度设计的实现

本文阐述了精度设计是《互换性与测量技术》课程在机械基础系列课程教改中的准确定位。指出互换性与精度设计是两个完全不同的概念 ,互换性是不同于精度设计的另一种要求。对于典型的机械产品或零件的设计与制造 ,应首先进行合理的精度设计 ,再根据具体条件确定是否需要按照标准化的原则满足互换的要求。     

Asymptotic Normality in Mixtures of Power Series Distributions

Abstract.  The problem of estimating the individual probabilities of a discrete distribution is considered. The true distribution of the independent observations is a mixture of a family of power series distributions. First, we ensure identifiability of the mixing distribution assuming mild conditions. Next, the mixing distribution is estimated by non-parametric maximum likelihood and an estimator for individual probabilities is obtained from the corresponding marginal mixture density. We establish asymptotic normality for the estimator of individual probabilities by showing that, under certain conditions, the difference between this estimator and the empirical proportions is asymptotically negligible. Our framework includes Poisson, negative binomial and logarithmic series as well as binomial mixture models. Simulations highlight the benefit in achieving normality when using the proposed marginal mixture density approach instead of the empirical one, especially for small sample sizes and/or when interest is in the tail areas. A real data example is given to illustrate the use of the methodology.     

On UMVU estimators and Bhattacharya bounds in exponential distributions

A subfamily of exponential distributions is considered and it is shown that the variance of the UMVU estimator of an estimable function g(θ) having power series expansion is the limit of Bhattacharya bounds.     

ON THE GENERALIZED RAO-RUBIN CONDITION and SOME VARIANTS

This paper gives a characterization of some members of the compound Poisson family of distributions based on the generalized Rao-Rubin condition. By considering some variants of this condition and using power series arguments, characterizations of the Poisson distribution are also obtained.     

The variability of rainfall acidity

Two years of rainfall acidity data for the eastern United States were analyzed. The data consist of rainfall-event pH measurements from a nine station monitoring network. A spatio-temporal stochastic model, including deterministic components for seasonal variation and rainfall washout, and stochastic components for spatial, temporal, and measurement variation, was fitted to the data. The fitted autocorrelation structure from this model was used, in the process known as Kriging, to obtain BLUE contour maps of seasonal and rainfall adjusted yearly average pH over the monitoring region.     

Extreme-value analysis of canadian wind speeds

Estimates of extreme winds are essential for engineering design, but in preparing such estimates major statistical issues are encountered. In this case study, the analysts were provided with hourly readings on wind speed, wind direction, and barometric pressure at five Canadian stations for observation periods ranging over several recent decades. Their assignment was to calculate point and interval estimates of 10-, 20-, 50-, and 100-year return values (i.e., upper fractiles) for the wind speeds at these stations.     

Bayesian break-point forecasting in parallel time series,with application to university admissions

A regular supply of applicants to Queen's University in Kingston, Ontario is provided by 65 high schools. Each high school can be characterized by a series of grading standards which change from year to year. To aid admissions decisions, it is desirable to forecast the current year's grading standards for all 65 high schools using grading standards estimated from past year's data. We develop and apply a Bayesian break-point time-series model that generates forecasts which involve smoothing across time for each school and smoothing across schools. “Break point” refers to a point in time which divides the past into the “old past” and the “recent past” where the yearly observations in the recent past are exchangeable with the observations in the year to be forecast. We show that this model works fairly well when applied to 11 years of Queen's University data. The model can be applied to other data sets with the parallel time-series structure and short history, and can be extended in several ways to more complicated structures.     

Large sample interval estimation for the logarithmic series distribution

It is assumed that a large random sample of fixed size n is drawn from a logarithmic series distribution with parameter $tH and that it is desired to estimate e by means of two-sided confidence interval. Using asymptotic results, charts of confidence intervals are prepared for n = 50, 100, 200, 500 and 1,000, and confidence coefficients 0.90 and 0.95.