The Use of Meteorological Data in the Prediction of Tropical Cyclones

Recent studies have shown that the ability to forecast tropical cyclone motion and intensity has not improved significantly over the past decade, in spite of apparent advances in the statistical and dynamical modeling of the motion- and intensity-change processes. This static situation has focused attention on the meteorological data and analysis techniques that provide input to these models. The quality and quantity of data have apparently not kept pace with the modeling efforts; in fact, increased sophistication in modeling alone may be counterproductive. Steps are being taken in the meteorological community to correct this problem. This study examines the entire data problem, discusses the remedial measures being taken, and points out some of the pitfalls in the statistical use of meteorological data.     

Model Reduction for Prediction in Regression Models

We look at prediction in regression models under squared loss for the random x case with many explanatory variables. Model reduction is done by conditioning upon only a small number of linear combinations of the original variables. The corresponding reduced model will then essentially be the population model for the chemometricians' partial least squares algorithm. Estimation of the selection matrix under this model is briefly discussed, and analoguous results for the case with multivariate response are formulated. Finally, it is shown that an assumption of multinormality may be weakened to assuming elliptically symmetric distribution, and that some of the results are valid without any distributional assumption at all.     

基于copula回归模型的损失预测

在非寿险损失预测的广义线性模型中,通常假设损失次数与损失强度相互独立,事实上二者之间往往存在一定的相依关系,可通过copula函数来刻画.在损失已经发生的条件下,假设损失次数服从零截断泊松分布,损失强度服从伽玛分布,可以建立损失次数与损失强度相互依赖的copula回归模型.把损失强度的分布扩展到逆高斯分布,并将此模型应用于一组车险保单数据进行实证研究.结果表明:该模型不但在损失预测方面优于独立假设下的广义线性模型,而且也优于损失强度服从伽马分布假设下的copula回归模型.     

Estimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models

In studies that produce data with spatial structure, it is common that covariates of interest vary spatially in addition to the error. Because of this, the error and covariate are often correlated. When this occurs, it is difficult to distinguish the covariate effect from residual spatial variation. In an i.i.d. normal error setting, it is well known that this type of correlation produces biased coefficient estimates, but predictions remain unbiased. In a spatial setting, recent studies have shown that coefficient estimates remain biased, but spatial prediction has not been addressed. The purpose of this paper is to provide a more detailed study of coefficient estimation from spatial models when covariate and error are correlated and then begin a formal study regarding spatial prediction. This is carried out by investigating properties of the generalized least squares estimator and the best linear unbiased predictor when a spatial random effect and a covariate are jointly modelled. Under this setup, we demonstrate that the mean squared prediction error is possibly reduced when covariate and error are correlated.     

Explained Variation and Predictive Accuracy in General Parametric Statistical Models: The Role of Model Misspecification

When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis.     

Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty

This article presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction are explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. This article outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson errors-in-variable generalized linear model, it has been shown in certain cases that LP produces better results than already known methods.     

Permutation Methods for Comparing the Accuracy of Nested Prediction Models in Survival Analysis

When making patient-specific prediction, it is important to compare prediction models to evaluate the gain in prediction accuracy for including additional covariates. We propose two statistical testing methods, the complete data permutation (CDP) and the permutation cross-validation (PCV) for comparing prediction models. We simulate clinical trial settings extensively and show that both methods are robust and achieve almost correct test sizes; the methods have comparable power in moderate to large sample situations, while the CDP is more efficient in computation. The methods are also applied to ovarian cancer clinical trial data.     

Optimal Prediction in Stochastic Regression Models with Application to the Analysis of Repeated Surveys

Several results relating to the optimal prediction of regression coefficients and random variables under a general linear model with stochastic coefficients are presented. These results are then applied to the analysis of repeated sample surveys over time. In particular, if the finite population can be modelled by a superpopulation model, a fully efficient method for the analysis of repeated surveys is proposed.     

Assessing One-Step-Ahead Prediction Error Based on the Median for First-Order Autoregressive Models in the Presence Of Outliers

The prediction of the one-step-ahead observation of the first-order autoregressive process in the presence of outliers is considered. The mean square of the prediction error is obtained based on the median estimator of the model parameter for a stationary process. Monte Carlo simulation methods are employed to investigate the performance of the proposed estimator as well as the conventional ordinary least squares estimators proposed by Zhang and Shaman (1995 Zhang , P. , Shaman , P. ( 1995 ). Assessing prediction error in autoregressive models . Trans. Amer. Mathemat. Soc. 347 : 627 – 637 .[Crossref], [Web of Science ®] , [Google Scholar]) and Kabaila and He (1999 Kabaila , P. , He , Z. ( 1999 ). On assessing prediction error in autoregressive models . J. Time Ser. Anal. 20 : 663 – 670 .[Crossref] , [Google Scholar]) for a process without outliers. The results show that the proposed method outperforms the conventional method. These conclusions are substantiated with results from actual datasets.     

On the Smoothness of Projections in Statistical Models

Here we examine the existence of the projection of a probability measure in a parametric statistical model. Once, in a general framework, the existence of the projection is established, we consider the problem from a statistical point of view, modeling a parametric statistical model as a convenient manifold.     

陕西高校各类教育提升全人素质的预测模型

采用"高校全人素质量表"随机抽样省部属8所高校4 953名大学生,对调查数据做SPSS多元回归参数估计和AMOS结构方程模型构建。结果表明,德育教育和专业教育分别对大学生全人素质16项指数产生显著正效应;德育教育"立德效应"主提升德道品质、心身健康、人生意义等素质;专业教育"树人效应"主提升求知钻研、学术探索、技术能力、语言思维等素质。研究结果为陕西高校全人素质教育提供了实证参数和理论依据。     

Exploring New Models for Population Prediction in Detecting Demographic Phase Change for Sparse Census Data

The logistic model has some limitations when applied to the sparse census data sets, typically available for developing countries. In such a situation, the relative growth rates (RGR) exhibit some unusual trends which are different from the common decreasing trend of logistic law. To explain such irregular trends we extend the logistic law by incorporating nonlinear positive and negative feedback terms. We performed RGR modelling as a function of time, as the size covariate model is not analytically solvable and the underlying model is better identifiable in the former case. It can also detect the demographic phase change point of developing country.     

Statistical Properties of Threshold Models

This article focuses the attention on the Self Exciting Threshold Autoregressive Moving Average model (SETARMA) proposed in Tong (1983 Tong , H. ( 1983 ). Threshold Models in Nonlinear Time Series Analysis . London : Springer-Verlag .[Crossref] , [Google Scholar]). The stochastic structure of the model is discussed and different specifications are presented. Starting from one of them, we give sufficient conditions for the weak stationarity of the model that are discussed and critically compared to other results given in literature. In particular, after showing that the SETARMA model belongs to the class of the Random Coefficients Autoregressive models, widely discussed in Nicholls and Quinn (1982 Nicholls , D. F. , Quinn , B. G. (1982). Random Coefficients Autoregressive Models. An Introduction . New York : Springer-Verlag.[Crossref] , [Google Scholar]), we give some issues on the weak stationarity of its stochastic structure that are more general than those given in the existing literature and appear not affected by the moving average component.     

论社会经济的统计预测

一种意见认为,统计是研究当前的事实,预测是研究将来的情况,所以统计工作兼顾预测实是越俎代庖,顾不了也顾不好。另一种意见认为,社会经济预测的内容很广泛,凡属于未来学的问题,预测都要加以研究,既有定量预测也有定性预测,在定量预测中既有统计预测也有非统计预测,所以预测工作最好由业务机关或学术团体去做。     

The Limited Role of Formal Statistical Inference in Scientific Inference

ABSTRACT

Such is the grip of formal methods of statistical inference—that is, frequentist methods for generalizing from sample to population in enumerative studies—in the drawing of scientific inferences that the two are routinely deemed equivalent in the social, management, and biomedical sciences. This, despite the fact that legitimate employment of said methods is difficult to implement on practical grounds alone. But supposing the adoption of these procedures were simple does not get us far; crucially, methods of formal statistical inference are ill-suited to the analysis of much scientific data. Even findings from the claimed gold standard for examination by the latter, randomized controlled trials, can be problematic.

Scientific inference is a far broader concept than statistical inference. Its authority derives from the accumulation, over an extensive period of time, of both theoretical and empirical knowledge that has won the (provisional) acceptance of the scholarly community. A major focus of scientific inference can be viewed as the pursuit of significant sameness, meaning replicable and empirically generalizable results among phenomena. Regrettably, the obsession with users of statistical inference to report significant differences in data sets actively thwarts cumulative knowledge development.

The manifold problems surrounding the implementation and usefulness of formal methods of statistical inference in advancing science do not speak well of much teaching in methods/statistics classes. Serious reflection on statistics' role in producing viable knowledge is needed. Commendably, the American Statistical Association is committed to addressing this challenge, as further witnessed in this special online, open access issue of The American Statistician.     

The Bootstrap and Kriging Prediction Intervals

Kriging is a method for spatial prediction that, given observations of a spatial process, gives the optimal linear predictor of the process at a new specified point. The kriging predictor may be used to define a prediction interval for the value of interest. The coverage of the prediction interval will, however, equal the nominal desired coverage only if it is constructed using the correct underlying covariance structure of the process. If this is unknown, it must be estimated from the data. We study the effect on the coverage accuracy of the prediction interval of substituting the true covariance parameters by estimators, and the effect of bootstrap calibration of coverage properties of the resulting 'plugin' interval. We demonstrate that plugin and bootstrap calibrated intervals are asymptotically accurate in some generality and that bootstrap calibration appears to have a significant effect in improving the rate of convergence of coverage error.     

Decomposition of Prediction Error in Multilevel Models

ABSTRACT

We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y _*j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study that spans the various combinations of level-1 (individual) and level-2 (group) sample sizes and different intraclass correlation values. Additionally, analytical results present the increase in predicted mean square error (PMSE) with respect to prediction error bias. The components of prediction error provide information with respect to the cost of parameter estimation versus data imputation for predicting future values in a hierarchical data set. Specifically, the cost of parameter estimation is very small compared to data imputation.     

工业企业统计改革的目标模式探索

一、经济体制改革给企业统计工作提出了新的要求 建国以来,我国工业企业的统计工作,为国民经济计划管理和企业经营管理提供了大量的资料,作出了重要的贡献。但是,工业企业的统计工作,是适应高度集中的计划管理体制和产品经济的统计模式,在企业内部缺乏动力与活力。概括起来有:一是分散。企业内部的统计业务都分散在职能部门,各自向上级主管的“条条”负责,没有一个综合统计部门管理企业的全部统计工作。只有分散的纵向业务联系,没有横向的统一的组织管理和综合汇总,     

Statistical Issues in Weather Forecasting*

Research and operational applications in weather forecasting are reviewed, with emphasis on statistical issues. It is argued that the deterministic approach has dominated in weather forecasting, although weather forecasting is a probabilistic problem by nature. The reason has been the successful application of numerical weather prediction techniques over the 50 years since the introduction of computers. A gradual change towards utilization of more probabilistic methods has occurred over the last decade; in particular meteorological data assimilation, ensemble forecasting and post‐processing of model output have been influenced by ideas from statistics and control theory.     

股票市场波动预测的ARCH族模型选择

文章基于单步向前预测法,寻找对不同ARCH族波动预测模型进行选择的方法和评判标准,并以上证指数为例,根据有关评判标准,寻找适合我国股市的ARCH波动预测模型。