Early origins of the gradient: the relationship between socioeconomic status and infant mortality in the United States

Although relationships between social conditions and health have been documented for centuries, the past few decades have witnessed the emergence of socioeconomic gradients in health and mortality in most developed countries. These gradients indicate that health improves, although decreasingly so, at higher levels of socioeconomic status. To minimize problems with reverse causality, I tested competing hypotheses for observed socioeconomic gradients for infant mortality outcomes. I found no support for the income-inequality hypothesis and negligible support for the occupational-grade hypothesis. The results indicate that absolute material conditions are the most important determinants of socioeconomic effects on the risk of infant mortality and that while poverty has the most pronounced effect on risk, income is decreasingly salutary across the majority of the mortality gradient.     

Specification of objectives in decision problems

If a decision problem is said to be difficult, one usually assumes that there is a very complicated relationship between the decision and the corresponding outcome. The problem is considered as solved when this relationship has been sorted out and described. This means that the probelm of selecting the best outcome from a set of possible outcomes, is taken as trivial. The main, or only difficulty is to find the decision which will lead to the best of the possible outcomes. The author argues that the real difficulty may often be to specify a preference ordering over the set of outcomes. He illustrates the point with examples from economics, and discusses some investment problems, in which the outcome of a decision naturally can be considered as a stochastic process.     

Inequality indices and the starshaped principle of transfers

The evaluation of income distributions is usually based on the Pigou-Dalton (PD) principle which says that a transfer from any people to people who have less decreases economic inequality, i.e., increases the social evaluation index. We introduce two weaker principles of transfers which refer to a parameter θ. With the new principles, only those PD transfers increase the social evaluation index which take from the class of incomes above θ and give to the class below θ. The relative positions of individuals remain unchanged, and either no individual may cross the line θ (principle of transfers about θ) or some may do who have been situated next to it (starshaped principle of transfers at θ). θ may be a given constant, a function of mean income, or a quantile of the income distribution. The classes of indices which are consistent with these transfers are completely characterized, and examples are given.     

De copulis non est disputandum

Normal distribution of residuals is a traditional assumption in multivariate models. It is, however, not very often consistent with real data. Copulae allow for an extension of dependency models to nonellipticity and for separation of margins from the dependency. This paper provides a survey of copulae where different copula classes, estimation and simulation techniques and goodness-of-fit tests are considered. In the empirical section we apply different copulae to the static and dynamic Value-at-Risk of portfolio returns and Profit-and-Loss function.     

Confirmatory Factor Analysis of Ordinal Data Using Full‐Information Adaptive Quadrature

We conducted confirmatory factor analysis (CFA) of responses (N=803) to a self‐reported measure of optimism, using full‐information estimation via adaptive quadrature (AQ), an alternative estimation method for ordinal data. We evaluated AQ results in terms of the number of iterations required to achieve convergence, model fit, parameter estimates, standard errors (SE), and statistical significance, across four link‐functions (logit, probit, log‐log, complimentary log‐log) using 3–10 and 20 quadrature points. We compared AQ results with those obtained using maximum likelihood, robust maximum likelihood, and robust diagonally weighted least‐squares estimation. Compared to the other two link‐functions, logit and probit not only produced fit statistics, parameters estimates, SEs, and levels of significance that varied less across numbers of quadrature points, but also fitted the data better and provided larger completely standardised loadings than did maximum likelihood and diagonally weighted least‐squares. Our findings demonstrate the viability of using full‐information AQ to estimate CFA models with real‐world ordinal data.     

Three Steps Strategy to Search for Optimum Classification Trees

This article presents a new strategy to construct classification trees. According to the proposed scheme, we focused on keeping the record of sequences of each constructed classification tree; both in terms of splitting predictors and their splitting values in an array. So overall we have as many arrays as we have drawn samples. At this stage, a three steps strategy is introduced, which is used to search for the optimum classification tree. The proposed strategy provides comparable or improved results in terms of generalized error rates than tree and rpart (packages available for classification purposes in the R) using four of the well-known evaluation functions, that is, the Gini, the Entropy, the Twoing, and the Exponent-based function to split nodes for many real-life datasets.     

Homogeneity testing in a Weibull mixture model

The mixed Weibull distribution provides a flexible model to analyze random durations in a possibly heterogeneous population. To test for homogeneity against unobserved heterogeneity in a Weibull mixture model, a dispersion score test and a goodness-of-fit test are investigated. The empirical power of these tests is assessed and compared on a broad range of alternatives. It comes out that the dispersion score test, as it is based on a Weibull-to-exponential transformation, often breaks down. A simple new procedure is introduced for Weibull mixtures in scale, which combines the dispersion score test and the goodness-of-fit test. The new test is compared with several known procedures and shown to have a good overall power. To detect mixtures in shape and scale, a goodness-of-fit test is recommended. This research has been partially sponsored by a grant of the Deutsche Forschungsgemeinschaft. We thank Lars Haferkamp for computational assistance and Wilfried Seidel and a referee for their remarks on alternative test procedures.     

Testing for Homogeneity in an Exponential Mixture Model

This paper studies diagnostic procedures to test for homogeneity against unobserved heterogeneity in an exponential mixture model. The procedures include a dispersion score test, a likelihood ratio test, a moment likelihood approach and several goodness-of-fit tests. The paper compares the empirical power of these tests on a broad range of alternatives and proposes a new test that combines the dispersion score test with a properly chosen goodness-of-fit procedure; its empirical power comes close to the power of the best of the other tests.     

Selecting treatments: a decision theoretic approach

The paper looks at the problem of comparing two treatments, for a particular population of patients, where one is the current standard treatment and the other a possible alternative under investigation. With limited (finite) financial resources the decision whether to replace one by the other will not be based on health benefits alone. This motivates an economic evaluation of the two competing treatments where the cost of any gain in health benefit is scrutinized; it is whether this cost is acceptable to the relevant authorities which decides whether the new treatment can become the standard. We adopt a Bayesian decision theoretic framework in which a utility function is introduced describing the consequences of making a particular decision when the true state of nature is expressed via an unknown parameter θ (this parameter denotes cost, effectiveness, etc.). The treatment providing the maximum posterior expected utility summarizes the decision rule, expectations taken over the posterior distribution of the parameter θ.