Estimation of Hazard, Density and Survivor Functions for Randomly Censored Data

Maximum likelihood estimation and goodness-of-fit techniques are used within a competing risks framework to obtain maximum likelihood estimates of hazard, density, and survivor functions for randomly right-censored variables. Goodness-of- fit techniques are used to fit distributions to the crude lifetimes, which are used to obtain an estimate of the hazard function, which, in turn, is used to construct the survivor and density functions of the net lifetime of the variable of interest. If only one of the crude lifetimes can be adequately characterized by a parametric model, then semi-parametric estimates may be obtained using a maximum likelihood estimate of one crude lifetime and the empirical distribution function of the other. Simulation studies show that the survivor function estimates from crude lifetimes compare favourably with those given by the product-limit estimator when crude lifetimes are chosen correctly. Other advantages are discussed.     

BUILDING THE EVIDENCE BASE FROM LONGITUDINAL DATA

Survey data from cross-sectional snapshots give an incomplete picture of the social fabric. Without longitudinal information, planners, practitioners, social scientists and policy makers would be in the dark about dynamics, durations and pathways of human development. Longitudinal data collected in birth cohort studies give a better guide to long-term processes and outcomes and are more informative about the causal relations that are the drivers of disadvantage or success. The content and structure of Britain's Birth Cohort Studies are described to illustrate the challenges of creating and studying longitudinal evidence. Examples of findings and policies based on these prospective longitudinal studies are given, along with a discussion of the practical decisions that have to be confronted in undertaking them.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Quadratic estimators of covariance components in a multivariate mixed linear model

It is known that the Henderson Method III (Biometrics 9:226–252, 1953) is of special interest for the mixed linear models because the estimators of the variance components are unaffected by the parameters of the fixed factor (or factors). This article deals with generalizations and minor extensions of the results obtained for the univariate linear models. A MANOVA mixed model is presented in a convenient form and the covariance components estimators are given on finite dimensional linear spaces. The results use both the usual parametric representations and the coordinate-free approach of Kruskal (Ann Math Statist 39:70–75, 1968) and Eaton (Ann Math Statist 41:528–538, 1970). The normal equations are generalized and it is given a necessary and sufficient condition for the existence of quadratic unbiased estimators for covariance components in the considered model.     

Perceptions of and Responses to Stigma

In the last 10 years, interest in the concept of stigma has grown remarkably throughout the social sciences. Today, stigma is an important topic that bridges many disciplines, including sociology, psychology, social psychology, and public health. This literature review primarily addresses perceptions of and responses to stigma from the insider's or target's perspective. The topics examined in this review include the following: defining stigma, public opinion and attitudes toward the stigmatized, measurement of perceptions of stigma and discrimination, the coping strategies employed by stigmatized persons to deal with stigma, the impact of stigma in terms of psychological and behavioral outcomes, explanations of coping strategies and outcomes, and strategies and interventions to reduce stigma. As many of the concerns faced by stigmatized persons are universal, what has been learned in research about one stigmatized group can provide insight into another. Promising directions for future stigma-related research are identified and discussed.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Wer das Stabilisieren vergisst: Warum eine schöne Abschlusspräsentation noch kein gutes Ende bedeutet

Many organizational change projects fail — despite a well thought out concept, professional project management and a binding implementation timetable. The reason why the expected success doesn’t materialize often lies with the missing maintenance. Once the project is officially completed and the client and project manager withdraw, the manner in which the organization will take up these changes is seldom systematically dealt with. Difficulties often arise when no discrete stabilizing measures are planned. We have derived four central action areas which will be clarified by way of practical examples. It will also be shown what is important to pay attention to in the stabilization phase so that a lasting success of the organizational change project can be ensured.