Semiparametric Bayesian accelerated failure time model with interval-censored data

Many existing approaches to analysing interval-censored data lack flexibility or efficiency. In this paper, we propose an efficient, easy to implement approach on accelerated failure time model with a logarithm transformation of the failure time and flexible specifications on the error distribution. We use exact inference for the Dirichlet process without approximation in imputation. Our algorithm can be implemented with simple Gibbs sampling which produces exact posterior distributions on the features of interest. Simulation and real data analysis demonstrate the advantage of our method compared to some other methods.     

“以人为本”思想的时代内涵

本文通过对以人为本思想时代内涵的探讨,倡导人们用"人性"化的思维方式和行为方式对待世界,在人与外部世界的和谐互动和人性关切中实现自然与生命的相得益彰。     

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.     

International Migration and Religious Participation: The Mediating Impact of Individual and Contextual Effects1

Supported by previous empirical work, theory from sociology of religion and migration provide testable hypotheses in predicting changes in immigrant religious participation surrounding the migratory event. Due to data constraints, however, these hypotheses have escaped broad‐based analysis. Using the New Immigrant Survey (NIS), religious participation from pre‐ to postmigration time periods is found to decrease among recent immigrants to the United States. Individual‐level characteristics (i.e., gender, familial conditions, employment) do not substantially explain this decline; alternatively, contextual‐level factors (i.e., religious pluralism and religious concentration) partially mediate this drop in immigrant religiosity.     

Seasonality Tests

This article modifies and extends the test against nonstationary stochastic seasonality proposed by Canova and Hansen. A simplified form of the test statistic in which the nonparametric correction for serial correlation is based on estimates of the spectrum at the seasonal frequencies is considered and shown to have the same asymptotic distribution as the original formulation. Under the null hypothesis, the distribution of the seasonality test statistics is not affected by the inclusion of trends, even when modified to allow for structural breaks, or by the inclusion of regressors with nonseasonal unit roots. A parametric version of the test is proposed, and its performance is compared with that of the nonparametric test using Monte Carlo experiments. A test that allows for breaks in the seasonal pattern is then derived. It is shown that its asymptotic distribution is independent of the break point, and its use is illustrated with a series on U.K. marriages. A general test against any form of permanent seasonality, deterministic or stochastic, is suggested and compared with a Wald test for the significance of fixed seasonal dummies. It is noted that tests constructed in a similar way can be used to detect trading-day effects. An appealing feature of the proposed test statistics is that under the null hypothesis, they all have asymptotic distributions belonging to the Cramér–von Mises family.     

Ruin Probabilities of a Dual Markov-Modulated Risk Model

This article is devoted to studying a dual Markov-modulated risk model, which can properly represent, to some extent, surplus processes of companies that pay costs continuously and have occasional gains. We consider both the finite and infnite horizon ruin probabilities under this dual model. Upper and lower bounds of Lundberg type are derived for these ruin probabilities. We also obtain a time-dependent version of Lundberg type inequalities.     

Preliminary test of fit in a general class of conditionally heteroscedastic nonlinear time series

This article is concerned with a general class of conditionally heteroscedastic time series including possibly nonlinear and asymmetric autoregressive conditional heteroscedastic (ARCH) and generalized ARCH models. A problem of preliminary test of fit (PTF, hereafter) within the broad class under consideration is discussed. It is noted that contrary to usual tests in the literature of conditionally heteroscedastic time series, PTF does not require any specification of the conditional variance in advance. Based on the joint limit distributions of sample autocorrelations, a certain Portmanteau-type statistic for PTF is proposed, and its limit is shown to be a chi-square distribution. In addition, some simulation studies, under various innovations, are reported to support our theoretical results.     

Least-squares tests of time-series,invention effects with and without autocorrelations

Gottman's version of the Mann and Wald asymptotic test for intervention effects in time-series data is presented as a useful small sample procedure. A Monte Carlo simulaltion is conducted to evaluate the procedure for controlling Type I errors with varying values of autoregressive coefficients. Results indicate the procedure works better than Gottman's work originally indicated. However, in some cases error rates can be unacceptably high. Procedures for evaluating changes in level in the presence of autocorrelation and slope are suggested and evaluated.     

Interview Costs in the Secretary Problem

The effect of interview costs on the optimal selection strategy and on the chance of success in secretary problems with order k selection rules, both for a finite number of applicants and in the limiting case, is examined. Probabilistic reasoning is used and numerical examples given.     

A Simulation-Based Specification Test for Diffusion Processes

This article makes two contributions. First, we outline a simple simulation-based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be used, for example, to form predictive confidence intervals for time period t + τ, given information up to period t. Second, we use the simulation-based approach to construct a test for the correct specification of a diffusion process. The suggested test is in the spirit of the conditional Kolmogorov test of Andrews. However, in the present context the null conditional distribution is unknown and is replaced by its simulated counterpart. The limiting distribution of the test statistic is not nuisance parameter-free. In light of this, asymptotically valid critical values are obtained via appropriate use of the block bootstrap. The suggested test has power against a larger class of alternatives than tests that are constructed using marginal distributions/densities. The findings of a small Monte Carlo experiment underscore the good finite sample properties of the proposed test, and an empirical illustration underscores the ease with which the proposed simulation and testing methodology can be applied.