A Bayesian look at diagnostics in the univariate linear model.

This paper develops diagnostics for data thought to be generated in accordance with the general univariate linear model. A first set of diagnostics is developed by considering posterior probabilities of models that dictate which of k observations form a sample of n observations (kspurious and outlying observations; posteriors of models; leverage; Kullback-Leibler measures; outlying and influential observations;

Comparing probabilistic methods for outlier detection.

This paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived.Diagnostic; Posterior and Predictive distributions; Leverage; Linear models;

A bayesian approach for predicting with polynomial regresión of unknown degree.

This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging. It is shown that Bayesian model averaging provides a closer relationship between the theoretical coverage of the high density predictive interval (HDPI) and the observed coverage than those corresponding to selecting the best model. The performance of the different procedures are illustrated with simulations and some known engineering data

Comparing probabilistic methods for outlier detection

This paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived

A Bayesian look at diagnostics in the univariate linear model

This paper develops diagnostics for data thought to be generated in accordance with the general univariate linear model. A first set of diagnostics is developed by considering posterior probabilities of models that dictate which of k observations form a sample of n observations (k < n/2) are spuriously generated, giving rise to the possible outlyingness of the k observations considered. This in turn gives rise to diagnostics to help assess (estimate) the value of k. A second set of diagnostics is found by using the Kullback-Leibler symmetric divergence, which is found to generate measures of outlyingness and influence. Both sets of diagnostics are compared and related to each other and to other diagnostic statistics suggested in the literature. An example to illustrate to the use of these diagnostic procedures is included

ENRICHING JUDICIAL INDEPENDENCE: SEEKING TO IMPROVE THE RETENTION VOTE PHASE OF AN APPOINTIVE SELECTION SYSTEM

This article discusses the problems and potential solutions with the system of judicial appointment in the state of Nebraska. The article focuses on how improving public awareness about the existing system, its goals, and its current weaknesses, and implementing steps to address those weaknesses, will help to keep everyone moving toward the best possible system. While changing attitudes and interest in judicial retention elections is certainly not an easy task, it is only through seeking such change that reformers of an elective retention system can hope to near its potential effectiveness

A Bayesian Approach for Predicting with Polynomial Regresión of Unknown Degree.

This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging. It is shown that Bayesian model averaging provides a closer relationship between the theoretical coverage of the high density predictive interval (HDPI) and the observed coverage than those corresponding to selecting the best model. The performance of the different procedures are illustrated with simulations and some known engineering data.

SERVIR Science Applications for Capacity Building

SERVIR is a regional visualization and monitoring system using Earth observations to support environmental management, climate adaptation, and disaster response in developing countries. SERVIR is jointly sponsored by NASA and the U.S. Agency for International Development (USAID). SERVIR has been instrumental in development of science applications to support the decision-making and capacity building in the developing countries with the help of SERVIR Hubs. In 2011, NASA Research Opportunities in Space and Earth Sciences (ROSES) included a call for proposals to form SERVIR Applied Sciences Team (SERVIR AST) under Applied Sciences Capacity Building Program. Eleven proposals were selected, the Principal Investigators of which comprise the core of the SERVIR AST. The expertise on the Team span several societal benefit areas including agriculture, disasters, public health and air quality, water, climate and terrestrial carbon assessments. This presentation will cover the existing SERVIR science applications, capacity building components, overview of SERVIR AST projects, and anticipated impacts

Further Developments in Dynamic Focusing

Dynamic focusing has been proposed as a way to eliminate a conventional collimation and final focus system in linear colliders, and is a scheme that is more readily extended to colliders at several TeV center-of-mass energy. In this paper we examine several outstanding issues, in particular, the optimization of the lens and main beam parameters. Simulations of the lens-lens, lens-main, and main­main beam collisions using a modified version of the GUINEAPIG beam­beam code are in progress