On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Women and Marital Breakdown in South India : Reconstructing Homes, Bonds and Persons

Women and Marital Breakdown in South India: Reconstructing Homes, Bonds and Persons is an ethnographic analysis of the situation of divorced and separated women and their families in the South Indian city of Bangalore. The study is based on 16 months of anthropological fieldwork, i.e., participant observation and life history interviews among 50 divorced and separated women from different socio-religious backgrounds in their homes, in the women s organisations and in the Family Court. The study follows the divorced and separated women from their natal homes to their affinal homes through homelessness and legal battles to their reconstructed natal, affinal or single homes in order to find out what it means to be a person within hierarchical gender and kinship relations in South India. Marital breakdown impacts on kin relations and discloses the existing gender relations and power structure through its consequences. It makes the transformability of relational personhood as well as the transformability of relational society and culture visible. Although the study reveals the painful history of women s ill-treatment in marriage, family and kinship systems, it also demonstrates the women s rejection of the domination; and shows their ability to re-negotiate and promote changes not only to their own positions but to the whole hierarchical system as well. The study explores the divorced and separated women s manifold dilemmas, complicated legal battles, and endless arrangements when they have to struggle with the very practical problems of supporting themselves financially, finding and making a new home for themselves, and re-arranging relationships with their kin and friends. As marital breakdown fundamentally transforms the women s relational field, it forces them to recreate substitutive relations in a flexible way and, simultaneously, to re-construct themselves and their lives without a ready or positive cultural or behavioural template. This process reveals the agency of the divorced and separated women as well as shedding light on issues of gender and the cultural construction of the person in South India. This topical study explores the previously neglected subject of marital breakdown in India and shows the new meaning of kinship in South India.