Marginalized random-effects models for clustered binomial data through innovative link functions

AStA Advances in Statistical Analysis - Random-effects models are frequently used to analyze clustered binomial data. The direct computation of the marginal mean response, when integrated over the...     

A technical note on “A fuzzy set approach for R&D portfolio selection using a real options valuation model” by Wang and Hwang (2007)

In this paper, three critical issues with the paper “A fuzzy set approach for R&D portfolio selection using a real options valuation model”, coauthored by Wang and Hwang and published in Omega 2007 are addressed. Shortcomings of the original work are highlighted and corrective measures to improve the approach are proposed.     

Ridge Estimation under the Stochastic Restriction

In linear programming and modeling of an economic system, there may occur some linear stochastic artificial or unnatural manners, which may need serious attentions. These stochastic unusual uncertainty, say stochastic constraints, definitely cause some changes in the estimators under work and their behaviors. In this approach, we are basically concerned with the problem of multicollinearity, when it is suspected that the parameter space may be restricted to some stochastic restrictions. We develop the estimation strategy form unbiasedness to some improved biased adjustment. In this regard, we study the performance of shrinkage estimators under the assumption of elliptically contoured errors and derive the region of optimality of each one. Lastly, a numerical example is taken to determine the adequate ridge parameter for each given estimator.     

Analysis of over-dispersed count data with extra zeros using the Poisson log-skew-normal distribution

ABSTRACT

Mixed Poisson distributions are widely used in various applications of count data mainly when extra variation is present. This paper introduces an extension in terms of a mixed strategy to jointly deal with extra-Poisson variation and zero-inflated counts. In particular, we propose the Poisson log-skew-normal distribution which utilizes the log-skew-normal as a mixing prior and present its main properties. This is directly done through additional hierarchy level to the lognormal prior and includes the Poisson lognormal distribution as its special case. Two numerical methods are developed for the evaluation of associated likelihoods based on the Gauss–Hermite quadrature and the Lambert's W function. By conducting simulation studies, we show that the proposed distribution performs better than several commonly used distributions that allow for over-dispersion or zero inflation. The usefulness of the proposed distribution in empirical work is highlighted by the analysis of a real data set taken from health economics contexts.