Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational ...

An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness have received much attention in the model-based clustering literature recently, we investigate the use of a distribution that can deal with both varying tail-weight and peakedness of data. A family of parsimonious models is proposed using an eigen-decomposition of...

This paper studies market-makers, agents responsible for maintaining liquidity and orderly price transitions in markets. Market-makers include major firms making markets on global stock exchanges, as well as software agents that run behind the scenes on novel electronic markets like prediction markets. We use a sophisticated model of marketmaking to build richer agent-based models of markets an...

Revenue Management (RM) is a technique that applies economic principles to system variables in order to maximize revenue. The focus of hotel RM, as perceived by most hospitality practitioners and researchers, is room revenue maximization. Available software products do not use any high level information and do not use sophisticated prediction models. In this paper we propose a conceptual RM mod...

Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the “word of mouth” effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influenc...

An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech s...

Given a set of rating data for a set of items, determining the values of items is a matter of importance and various probability models have been proposed. The Plackett-Luce model is one of such models, which parametrizes the value of each item by a real valued preference parameter. In this paper, the Plackett-Luce model is generalized to cope with the grouped ranking observations such as movie...