the lake urmia and its satellite wetlands have been selected as a demonstration site for the united nations development program / global environment facility / department of environment conservation of iranian wetlands project. this project aims to demonstrate reduction of the major threats of this wetland protected area coordinated through an integrated management plan. a choice experiment was...

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...

The purpose of this study is to empirically investigate variation in the migration behaviour across individuals. Both a random parameters logit (RPL) and a standard logit model are employed. The migration models are estimated using a register-based data from Finland. The RPL model proves to be a flexible way to control for random variation in the parameters: the results suggest that many migrat...

Caussinus’s loglinear model of quasi symmetry has interesting connections with models for within-subject effects with repeated categorical measurement. For binary responses, Tjur (1982) showed that estimates of main effect parameters in the quasi-symmetry model are also conditional maximum likelihood estimates of item parameters for a fixed effects treatment of subject terms in the Rasch item r...

This paper is about estimating a random coefficients logit model in which the distribution of each coefficient characterized by finitely many parameters, some may be zero. The gives conditions under which, with probability approaching 1 as sample size increases, penalized maximum likelihood (PML) estimation adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters...

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed ...