Meta-analysis of the impact of age structure on fertility

The paper uses a meta-analysis to comparatively evaluate the literature addressing the aggregate relationship between a populations age structure and fertility, as hypothesized by Richard Easterlin. The analysis is based on 334 estimated effects retrieved from 19 studies. The results suggest that several factors undermine the empirical support of the Easterlinian age structure/fertility link. These include the neglect of income, the use of relative cohort size to characterize the age structure, mis-specifications of the relevant age-cohorts, as well as the functional form and estimation technique. The results also suggest that the sample of published estimates possibly suffers from two types of publication bias, an under-representation of insignificant effects for small samples, and bias towards supportive effects in the earlier years followed by a bias towards negative effects as the literature matured.All correspondence to: Brigitte Waldorf. The authors appreciate the insightful comments and suggestions from two anonymous reviewers. The research also benefited from discussions with participants of the International Colloquium on Meta-analysis in Economics, Free University, Amsterdam, The Netherlands, December 2002. Finally, we would like to thank David Brown, Meagan Cahill, Angela Donelson, Calvin Farris, and Melaney Seacat from the University of Arizona for their research assistance. Responsible editor: Junsen Zhang.     

Information in experiments and sufficiency

We define measures of information contained in an experiment which are by-products of the parametric measures of Fisher, Vajda, Mathai and Boekee and the non-parametric measures of Bhattacharyya, Rényi, Matusita, Kagan and Csiszár. We use these measures to compare sufficient experiments according to Blackwell's definition. In particular, we prove that if δ_X and δ_Y are two experiments and δ_X≥δ_Y then l_X≥l_y for all of the above measures.     

Quality of life indicators and program evaluation

Social indicators and program evaluation research have much in common. Quality of life work specifically raises the question of objective versus subjective variables, aggregate data versus distributional and individual issues, and the reference standards used in judgment and evaluation decisions. This article presents perspectives on these issues from the orientation of quality of life research and proposes that these approaches have relevance for program evaluation work.     

B2B下服务质量对关系质量的影响研究

在B2B背景下,以认证行业的顾客为调研对象,探究了服务质量对关系质量的影响.在已有的研究基础上,界定了服务质量的4维度结构和关系质量的3维度结构,构建了服务质量对关系质量的影响模型.对认证行业的721家顾客企业的实证研究表明,过程服务能力对关系质量有着正向的影响,结果质量对关系质量中的合作性、适应性有正向的影响,对关系氛围则没有显著影响,而过程交互质量与潜在服务能力对关系质量的影响不显著.     

Statistical Properties of a Modified Accelerated Design for Phase I Cancer Clinical Trials

We consider the semiparametric profile likelihood inference for the distribution function under doubly censored data. For further developments of the statistical inference based on the profile likelihood ratio and alternative tools such as the score or Wald-type inference, we discuss the structures of the profile likelihood estimators and their derivatives included in the score function and the Fisher function of the profile likelihood, establishing the consistencies of their estimators.     

A detection algorithm for the first jump time in sample trajectories of jump-diffusions driven by α-stable white noise

Abstract

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations.     

Likelihood-based cross-validation score for selecting the smoothing parameter in maximum penalized likelihood estimation

Maximum penalized likelihood estimation is applied in non(semi)-para-metric regression problems, and enables us exploratory identification and diagnostics of nonlinear regression relationships. The smoothing parameter A controls trade-off between the smoothness and the goodness-of-fit of a function. The method of cross-validation is used for selecting A, but the generalized cross-validation, which is based on the squared error criterion, shows bad be¬havior in non-normal distribution and can not often select reasonable A. The purpose of this study is to propose a method which gives more suitable A and to evaluate the performance of it.

A method of simple calculation for the delete-one estimates in the likeli¬hood-based cross-validation (LCV) score is described. A score of similar form to the Akaike information criterion (AIC) is also derived. The proposed scores are compared with the ones of standard procedures by using data sets in liter¬atures. Simulations are performed to compare the patterns of selecting A and overall goodness-of-fit and to evaluate the effects of some factors. The LCV-scores by the simple calculation provide good approximation to the exact one if λ is not extremeiy smaii Furthermore the LCV scores by the simple size it possible to select X adaptively They have the effect, of reducing the bias of estimates and provide better performance in the sense of overall goodness-of fit. These scores are useful especially in the case of small sample size and in the case of binary logistic regression.     

The Modeling and Seasonal Adjustment of Weekly Observations

Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time series model that allows the seasonal pattern to evolve over time and enables trend extraction and seasonal adjustment to be carried out by means of state-space filtering and smoothing algorithms. The method is illustrated with a Bank of England series on the money supply.