The Bayesian Approach offers the viable and rigorous solution, though there is also the added benefit of providing much-needed uncertainty and probability assessments in non-linear and non-Gaussian situations in a valid and rigorous way. Mortality and its various determinants have been traditionally studied in a regression modeling framework. Initial studies mostly used the usual linear regression models which, however, are not appropriate in situations where the mortality information is given by a binary indicator of death or alive. Binary regression models (logit and probit) are, therefore, a logical alternatives. There are, however, problems, with logit and probit models, namely, that they do not take into consideration the information on the survival time. Hence, most studies now utilize the survival analysis techniques. Recently, Fahrmeir and co-researchers at the LMU Munich have proposed a Bayesian Geo-Additive modeling framework which encompasses most of the known regression models and improves upon their shortcomings. The proposed model is also called Bayesian semi-parametric structured regression model.

Child mortality reflects a country's level of socio-economic development and quality of life. In developing countries, mortality rates are influenced by socio-economic, demographic and environmental variables. In this paper, we analyze the causes of child mortality in Ethiopia, with a particular emphasis on assessing possible time-variations in the effects of covariates, i.e. whether the effects of certain covariates vary with the age of the child. The analysis is based on micro data from the 2005 Demographic and health Survey. Employing a Bayesian Semi Parametric Probit Model for discrete time survival data and Markov-Chain Monte Carlo methods (MCMC), we find that there are several variables, including the age of the mother and the breast feeding duration whose effects exhibit distinct age-dependencies. In the case of breast feeding, this age dependency is intimately linked with the reasons for stopping breast feeding. Incorporating such age dependencies greatly improves the explanatory power of the model and yields new insights on the differential role of covariates on child survival.

In survival data analysis, the proportional hazard model was introduced by Cox (1972) in order to estimate the effects of different covariates influencing the time-to-event data. The proportional hazard model has been used extensively in biomedicine, reliability engineering and, recently, interest in its application in different areas of knowledge has increased. However, proportional hazard model makes a number of assumptions, which may be violated. The object of this article is to present a Bayesian analysis for survival models with frailty under additive framework for the hazard function in contrast to proportional hazard model. Frailty models in survival analysis deal with the unobserved heterogeneity among subjects. Gibbs sampling technique is used to assess the posterior quantities of interest. An illustrative analysis within the context of survival time data is given.

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

This book presents both theoretical contributions and empirical applications of advanced statistical techniques including geo-additive models that link individual measures with area variables to account for spatial correlation; multilevel models that address the issue of clustering within family and household; multi-process models that account for interdependencies over life-course events and non-random utilization of health services; and flexible parametric alternatives to existing intensity models. These analytical techniques are illustrated mainly through modeling maternal and child health in the African context, using data from demographic and health surveys. In the past, the estimation of levels, trends and differentials in demographic and health outcomes in developing countries was heavily reliant on indirect methods that were devised to suit limited or deficient data. In recent decades, world-wide surveys like the World Fertility Survey and its successor, the Demographic and Health Survey have played an important role in filling the gap in survey data from developing countries. Such modern demographic and health surveys enable investigators to make in-depth analyses that guide policy intervention strategies, and such analyses require the modern and advanced statistical techniques covered in this book. The text is ideally suited for academics, professionals, and decision makers in the social and health sciences, as well as others with an interest in statistical modelling, demographic and health surveys. Scientists and students in applied statistics, epidemiology, medicine, social and behavioural sciences will find it of value.

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.