Bayesian space-time modeling of malaria incidence in Sucre state, Venezuela

Malaria is a parasitic infectious tropical disease that causes high mortality rates in the tropical belt. In Venezuela, Sucre state is considered the third state with most disease prevalence. This paper presents a hierarchical regression log-Poisson space-time model within a Bayesian approach to represent the incidence of malaria in Sucre state, Venezuela, during the period 1990–2002 in 15 municipalities of the state. Several additive models for the logarithm of the relative risk of the disease for each district were considered. These models differ in their structure by including different combinations of social-economic and climatic covariates in a multiple regression term. A random effect that captures the spatial heterogeneity in the study region, and a CAR (Conditionally Autoregressive) component that recognizes the effect of nearby municipalities in the transmission of the disease each year, are also included in the model. A simpler version without including the CAR component was also fitted to the data. Model estimation and predictive inference was carried out through the implementation of a computer code in the WinBUGS software, which makes use of Markov Chain Monte Carlo (MCMC) methods. For model selection the criterion of minimum posterior predictive loss (D) was used. The Moran I statistic was calculated to test the independence of the residuals of the resulting model. Finally, we verify the model fit by using the Bayesian p-value, and in most cases the selected model captures the spatial structure of the relative risks among the neighboring municipalities each year. For years with a poor model fit, the t-Student distribution is used as an alternative model for the spatial local random effect with better fit to the tail behavior of the data probability distribution.     

A procedure for estimating the number of outliers

A new type of procedure for estimating the number of outliers in a sample is presented and compared with existing procedures. The probabilities of exact, under-, and overestimation with the different procedures are examined for two different contamination schemes.     

Comparison op some two sample tests for equality of variances

The F-test, F max-test and Bartlett's test are compared on the basis of power for the purpose of testing the equality of variances in two normal populations. The power of each test is expressed as a linear combination of F-probabilities. Bartlett's test is noted to be unbiased, UMPU, consistent against all alterna¬tives and the test which yields minimum length confidence intervals on the ratio of the variancesλ=σ₁ ² /σ² ₂ The two samples Bartlett critical values, although not recognized as such, are found in the works of other authors. Tables of the powers of each test are given for various values of λ, levels of significance a and the respective sample sizes, n₁ and n₂.     

Integer-parameter estimation in discrete distributions

Integer-parameter restriction quite often occurs naturally in real life situations. Here we consider the problem of deriving the maximum likelihood estimators (MLE) for the case in which the parameter is restricted to a positive integer. The usual asymptotic theory for the MLE does not hold good any more and each case needs individual attention for the derivation of these results,. The estimation problem in the case of Poisson, Binomial, and Poisson-Binomial bivariate model is investigated here, A simple method of deriving the MLE and the lower bound for the variance of the integer-parameter estimator is also discussed     

Bayes and empirical bayes estimation of the probability that z > x + y

Let X, Y and Z be independent random variables with common unknown distribution F. Using the Dirichlet process prior for F and squared erro loss function, the Bayes and empirical Bayes estimators of the parameters λ(F). the probability that Z > X + Y, are derived. The limiting Bayes estimator of λ(F) under some conditions on the parameter of the process is shown to be asymptotically normal. The aysmptotic optimality of the empirical Bayes estimator of λ(F) is established. When X, Y and Z have support on the positive real line, these results are derived for randomly right censored data. This problem relates to testing whether than used discussed by Hollander and Proshcan (1972) and Chen, Hollander and Langberg (1983).     

Another generalization of the logarithmic series and the geometric distributions

A new generalization of the logarithmic series distribution is presented based on a generalized negative binomial distribution obtained from a generalized Poisson distribution compounded with the truncated gamma distribution. By length biasing this generalized log-series distribution, another generalized geometric distribution is uresented. For the generalized log-series distribution, maximum likelihood estimators are developed and an example is presented for illustration.     

Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

It is widely accepted that the relationship between lightning wildfire occurrence and its influencing factors vary depending on the spatial scale of analysis, making the development of models at the regional scale advisable. In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning-caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning-caused fire in any 4 × 4-km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possible covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitude and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning-caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long-term forest fuel and fire management strategies.     

Food Well-Being in Older Adults: Effects of a Universal Non-contributory Pension in Mexico

Social Indicators Research - One of the few universal programs in Latin America is the Food Pension for Adults Older than 68 (PAAM) of Mexico City, which aims to reduce food insecurity while...     

Investigating the impact of Hurricane Maria on an ongoing birth cohort in Puerto Rico

Population and Environment - Prior to Hurricane Maria, Puerto Rico already had 200+ hazardous waste sites, significant contamination of water resources, and among the highest rates of preterm birth...