Exponentiated power Lindley power series class of distributions: Theory and applications

ABSTRACT

We introduce a new four-parameter generalization of the exponentiated power Lindley (EPL) distribution, called the exponentiated power Lindley power series (EPLPS) distribution. The new distribution arises on a latent complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the minimum lifetime value among all risks. The distribution exhibits a variety of bathtub-shaped hazard rate functions. It contains as particular cases several lifetime distributions. Various properties of the distribution are investigated including closed-form expressions for the density function, cumulative distribution function, survival function, hazard rate function, the rth raw moment, and also the moments of order statistics. Expressions for the Rényi and Shannon entropies are also given. Moreover, we discuss maximum likelihood estimation and provide formulas for the elements of the Fisher information matrix. Finally, two data applications are given showing flexibility and potentiality of the EPLPS distribution.     

High-order extensions of the Double Chain Markov Model

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.     

IFLA Library Reference Model,RDA, and Serials in a Nutshell

Abstract

IFLA Library Reference Model (IFLA LRM) represents the consolidation of Functional Requirements for Bibliographic Records (FRBR) and the two other conceptual models of the Functional Requirements (FR) family, the Functional Requirements for Authority Data (FRAD) and the Functional Requirements for Subject Authority Data (FRSAD). IFLA LRM includes a specific section (5.8) devoted to serials. The RDA Steering Committee adopted IFLA LRM as the conceptual model for the development of RDA in November 2016. The 3R (RDA Toolkit Restructure and Redesign) Project’s purpose is partly to make RDA compatible with the IFLA LRM. A beta version of the English-language content of the RDA Toolkit, which incorporates the IFLA LRM, was released in June 2018. This column explores, in lay terms, serials in relation to the IFLA LRM and the RDA Toolkit Restructure and Redesign (3R) Project. The beta version of the restructured and redesigned RDA Toolkit introduces several new concepts relating to serials.     

Book reviews

Donald J. Koosis: Statistics. A Self-Teaching Guide, Fourth Edition. Wiley 1997, ISBN 0-471-14688-9

Christopher C. Heyde: Quasi-Likelihood and Its Applications, Springer Series in Statistics, 1997, pp. 235, [ISBN 0-387-98225-61

Jeffrey S. Simonoff: Smoothing Methods in Statistics, Springer Series in Statistics, 1996, pp. 338

Andr´e I. Khuri, Thomas Mathew and Birmal K. Sinha: Statistical Tests for Mixed Linear Models, Wiley Series in Probability and Statistics, 1998, pp. 352, PSBN 0-471-1 5653-11

Ronald Christensen: Log-Linear Models and Logistic Regression, Springer Texts in Statistics, 1997, pp. 483, [ISBN 0-387-98247-71

E. L. Lehmann: Testing Statistical Hypotheses, Springer Texts in Statistics, 1997, pp. 600, [ISBN 0-387-94919-41

S. R. Searle, G. Casella and Ch. E. McCulloch: Variance Components, Wiley and Sons, 1992, pp. 496, [ISBN 0-471-62162-51

B. J. T. Morgan: Analysis of Quantal Response Data, Chapman & Hall, 1992, pp. 51 1

Carl D. Huberty: Applied Discriminant Analysis, Wiley and Sons, 1994, pp. 490

C. R. Rao and H. Toutenburg: Linear Models. Least Squares and Alternatives, Springer Series in Statistics, 1995, 188 pp., [ISBN 0-387-94562-81     

Model averaging for M-estimation

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results.     

Cointegration Detection Using Dynamic Factor Models

A method of information-criterion-based cointegration detection using dynamic factor models is proposed. The results of the data-based and non data-based Monte Carlo simulations suggest that this method is as effective as conventional hypothesis-testing methods. In the proposed method, an observed multivariate time series is described in terms of common stochastic trends plus stationary autoregressive cycles. Then the best model is selected from among alternative models obtained by changing the number of common stochastic trends, on the basis of information criteria. Consequently, the cointegration rank is determined on the basis of the selected model. Two advantages of the proposed method are also discussed.     

Comparing Possibly Misspecified Forecasts

Abstract

Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient for the ranking by any consistent loss function. This article shows, via analytical results and realistic simulation-based analyses, that the presence of misspecified models, parameter estimation error, or nonnested information sets, leads generally to sensitivity to the choice of (consistent) loss function. Thus, rather than merely specifying the target functional, which narrows the set of relevant loss functions only to the class of loss functions consistent for that functional, forecast consumers or survey designers should specify the single specific loss function that will be used to evaluate forecasts. An application to survey forecasts of U.S. inflation illustrates the results.     

Semiparametric multiple kernel estimators and model diagnostics for count regression functions

Abstract

This study concerns semiparametric approaches to estimate discrete multivariate count regression functions. The semiparametric approaches investigated consist of combining discrete multivariate nonparametric kernel and parametric estimations such that (i) a prior knowledge of the conditional distribution of model response may be incorporated and (ii) the bias of the traditional nonparametric kernel regression estimator of Nadaraya-Watson may be reduced. We are precisely interested in combination of the two estimations approaches with some asymptotic properties of the resulting estimators. Asymptotic normality results were showed for nonparametric correction terms of parametric start function of the estimators. The performance of discrete semiparametric multivariate kernel estimators studied is illustrated using simulations and real count data. In addition, diagnostic checks are performed to test the adequacy of the parametric start model to the true discrete regression model. Finally, using discrete semiparametric multivariate kernel estimators provides a bias reduction when the parametric multivariate regression model used as start regression function belongs to a neighborhood of the true regression model.     

海洋强国战略目标下海洋经济统计核算的综述

海洋经济统计核算体系研究,是推动海洋强国建设及海洋经济高质量发展深入研究的关键基础工作,对于夯实我国海洋经济数据基础、综合客观反映我国海洋经济发展状况具有重要意义。本文从海洋经济的概念内涵出发,系统梳理海洋经济统计核算体系的演变历程,深入解析了海洋经济统计核算指标体系构成,并对海洋经济统计核算方法进行了重点剖析,以期推进海洋强国战略目标下我国海洋经济统计核算体系的优化和完善。     

论强纲领中的因果性模型问题

布鲁尔强纲领中所反对的目的论模型来源于巴恩斯提出的目的论模式，他们都拒绝将科学的发展看作真理进化过程，认为作为一种信念的科学知识应当纳入知识社会学的研究领域。布鲁尔进一步提出了与目的论模型相对立的因果性模型，并构建出科学知识社会学应当遵守的四条“强纲领”，力图促使知识社会学成为一门普遍性的学科。布鲁尔声称强纲领的因果性模型与目的论模型是两种相互排斥的形而上学立场，但实际上因果性模型与目的论模型并不完全排斥，甚至还有所交融；同时，他关于因果性模型的论证与辩护也存在诸多问题，是一种不彻底的“强纲领”。