In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare th...

In original Correspondence LDA (Corr-LDA) [1] the image region is generated by a multivariate Gaussian distribution. We replace this Gaussian distribution by a multinomial distribution, which gives a modified algorithm for parameter estimation. 1 Corr-LDA with Multinomial Generative Process To keep the consistence, we denote visual word and auditory word as sensory word. Thus, A document (a cap...

We introduce a semiparametric “tubular neighborhood” of a parametric model in the multinomial setting. It consists of all multinomial distributions lying in a distance-based neighborhood of the parametric model of interest. Fitting such a tubular model allows one to use a parametric model while treating it as an approximation to the true distribution. In this paper, the Kullback–Leibler distanc...

Analysis of somatic mutations in V regions of Ig genes is important for understanding various biological processes. It is customary to estimate Ag selection on Ig genes by assessment of replacement (R) as opposed to silent (S) mutations in the complementary-determining regions and S as opposed to R mutations in the framework regions. In the past such an evaluation was performed using a binomial...

In probability and statistics, uncertainty is usually quantified using single-valued probabilities satisfying Kolmogorov’s axioms. Generalisation of classical probability theory leads to various less restrictive representations of uncertainty which are collectively referred to as imprecise probability. Several approaches to statistical inference using imprecise probability have been suggested, ...

This article introduces a Markov chain Monte Carlo (MCMC) method for sampling the parameters of a multinomial logit model from their posterior distribution. Let yi ∈ {0, . . . ,M} denote the categorical response of subject i with covariates xi = (xi1, . . . , xip) T . Let X = (x1, . . . ,xn) T denote the design matrix, and let y = (y1, . . . , yn) T . Multinomial logit models relate yi to xi th...

In the current paper, we propose a new utility-consistent modeling framework to explicitly link a count data model with an event type multinomial choice model. The proposed framework uses a multinomial probit kernel for the event type choice model and introduces unobserved heterogeneity in both the count and discrete choice components. Additionally, this paper establishes important new results ...