Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

A two-phase approach for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Management Approaches to Merger Evoked Cultural Change and Acculturation Outcomes

In a study of merger-evoked cultural change in three organizations, quantitative and qualitative data were collected from individuals at all employment levels in both merger partners within each organization. Results were that most individuals perceived that the merger had impacted significantly on them personally. There was, however, a perceived lack of congruence between the organizational cultures of merging partners, resulting in cultural clashes and significant changes to the organizations' organizational cultures. More specifically, outcomes for both individuals and the subsequent acculturation following the mergers were related to the approach adopted to manage the merger process: incremental, immediate, or indifferent.

A two-phase strategy for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We previously evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach to delineate breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

The Swiss heroin trials: testing alternative approaches

Over half a million heroin misusers receive oral methadone maintenance treatment world-wide1 but the maintenance prescription of injectable opioid drugs, like heroin, remains controversial. In 1992 Switzerland began a large scale evaluation of heroin and other injectable opiate prescribing that eventually involved 1035 misusers. 2 3 The results of the evaluation have recently been reported.4 These show that it was feasible to provide heroin by intravenous injection at a clinic, up to three times a day, for seven days a week. This was done while maintaining good drug control, good order, client safety, and staff morale. Patients were stabilised on 500 to 600 mg heroin daily without evidence of increasing tolerance. Retention in treatment was 89% at six months and 69% at 18 months.4 The self reported use of non-prescribed heroin fell signifianctly, but other drug use was minimally affected. The death rate was 1% per year, and there were no deaths from overdose among participants . . . [Full text of this article]

Challenges and approaches to estimating mortality attributable to the use of selected illicit drugs

A number of unique challenges are faced when attempting to estimate mortality attributable to illicit drugs. The hidden nature of illicit drug use creates difficulties in quantifying the prevalence of such use; identifying adverse health effects associated with exposure, and calculating the risk of these effects. The use of cohort studies of drug users allows the identification of causes of mortality associated with drug use and the determination of the risk of these causes. This risk estimate can then be used in conjunction with estimates of the prevalence of drug use to, extrapolate the burden of mortality. We identify a number of such studies and present some solutions to the major challenges faced when attempting to estimate the global burden of mortality attributable to illicit drug use. Copyright (C) 2001 S. Karger AG, Basel.

A context noise model of episodic word recognition

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Analysis of Approaches to Controllership in Mater`s Thesis from Post-graduate Programs in Accounting Sciences

The objective of the Study is to analyze approaches in master`s thesis in Brazilian Post-Graduate Programs in Accounting Sciences in relation to Controllership, in terms of their conceptual, procedural and organizational aspects, as proposed by Borinelli (2006). The research is descriptive and it uses a quantitative approach. The sample consists of 26 master`s thesis which have the word ""Controllership"" in their titles. Resulting from analysis, in Perspective I (conceptual aspects), in which the elements of definition, object of study and relationship with other sciences were referenced, consensus among authors of the master`s thesis was not verified. In Perspective II (procedural aspects), which deals with activities and functions of Controllership by means of how they materialize as areas of knowledge within organizations, it was observed that the approach in the master`s thesis is quite differentiated in terms of the scope of activities. In relation to Perspective III (organizational aspects), there is also no consensus about what constitutes typical Controllership activities, but master`s thesis do include in the definition of Controllership the idea that it is a service or function of information. It was concluded that the approach to controllership, in terms of its conceptual, procedural and organizational aspects is similar to the elements proposed by Borinelli (2006).

Bayesian analysis of the error correction model

This paper presents a method for estimating the posterior probability density of the cointegrating rank of a multivariate error correction model. A second contribution is the careful elicitation of the prior for the cointegrating vectors derived from a prior on the cointegrating space. This prior obtains naturally from treating the cointegrating space as the parameter of interest in inference and overcomes problems previously encountered in Bayesian cointegration analysis. Using this new prior and Laplace approximation, an estimator for the posterior probability of the rank is given. The approach performs well compared with information criteria in Monte Carlo experiments. (C) 2003 Elsevier B.V. All rights reserved.