A local linear estimation of conditional hazard function in censored data

A local linear estimator of the conditional hazard function in censored data is proposed. The estimator suggested in this paper is motivated by the ideas of Fan, Yao, and Tong (1996) and Kim, Bae, Choi, and Park (2005). The asymptotic distribution of the proposed estimator is derived, and some numerical results are also provided.     

To market, to market: the changing face of grocery retailing

Over the past few years, grocery retailing has undergone a tremendous transformation which was driven mainly by changing consumer characteristics. In order to accommodate these changes, the industry has responded in three ways. The most important of these is the introduction of new technology, particularly efficient consumer response (ECR). The second change has to do with innovations in product and/or service offerings. The final transformation is the organisational restructuring and the increasing number of mergers and acquisitions in the industry at both the manufacturer and retailer levels. The implications of these transformations on the marketing mix, productivity and manufacturer-retailer relationships are discussed.     

《新约》"其他书信"的信仰观

"其他书信"的作者们,在<新约>中对基督信仰有着独特的理解.他们在基督论方面继续坚持对基督的再来、审判的末世论信仰.他们的信仰教义,通过和教会内外各种背离耶稣的教导、使徒的传统的学说的辩论日益明晰起来.另一方面,他们越来越意识到基督徒在世界中独特的寄居身份形象.这种形象,和他们在现实生活中遭遇的逼迫相关.     

On Two General Classes of Discrete Bivariate Distributions

In this article, we develop two general classes of discrete bivariate distributions. We derive general formulas for the joint distributions belonging to the classes. The obtained formulas for the joint distributions are very general in the sense that new families of distributions can be generated just by specifying the “baseline seed distributions.” The dependence structures of the bivariate distributions belonging to the proposed classes, along with basic statistical properties, are also discussed. New families of discrete bivariate distributions are generated from the classes. Furthermore, to assess the usefulness of the proposed classes, two discrete bivariate distributions generated from the classes are applied to analyze a real dataset and the results are compared with those obtained from conventional models.     

A comparison of alternative methods to construct confidence intervals for the estimate of a break date in linear regression models

This article considers constructing confidence intervals for the date of a structural break in linear regression models. Using extensive simulations, we compare the performance of various procedures in terms of exact coverage rates and lengths of the confidence intervals. These include the procedures of Bai (1997 Bai, J. (1997). Estimation of a change point in multiple regressions. Review of Economics and Statistics 79:551–563.[Crossref], [Web of Science ®] , [Google Scholar]) based on the asymptotic distribution under a shrinking shift framework, Elliott and Müller (2007 Elliott, G., Müller, U. (2007). Confidence sets for the date of a single break in linear time series regressions. Journal of Econometrics 141:1196–1218.[Crossref], [Web of Science ®] , [Google Scholar]) based on inverting a test locally invariant to the magnitude of break, Eo and Morley (2015 Eo, Y., Morley, J. (2015). Likelihood-ratio-based confidence sets for the timing of structural breaks. Quantitative Economics 6:463–497.[Crossref], [Web of Science ®] , [Google Scholar]) based on inverting a likelihood ratio test, and various bootstrap procedures. On the basis of achieving an exact coverage rate that is closest to the nominal level, Elliott and Müller's (2007 Elliott, G., Müller, U. (2007). Confidence sets for the date of a single break in linear time series regressions. Journal of Econometrics 141:1196–1218.[Crossref], [Web of Science ®] , [Google Scholar]) approach is by far the best one. However, this comes with a very high cost in terms of the length of the confidence intervals. When the errors are serially correlated and dealing with a change in intercept or a change in the coefficient of a stationary regressor with a high signal-to-noise ratio, the length of the confidence interval increases and approaches the whole sample as the magnitude of the change increases. The same problem occurs in models with a lagged dependent variable, a common case in practice. This drawback is not present for the other methods, which have similar properties. Theoretical results are provided to explain the drawbacks of Elliott and Müller's (2007 Elliott, G., Müller, U. (2007). Confidence sets for the date of a single break in linear time series regressions. Journal of Econometrics 141:1196–1218.[Crossref], [Web of Science ®] , [Google Scholar]) method.