On categorical time series models with covariates

Conditional and unconditional categorical regression models with missing covariates.

We consider methods for analyzing categorical regression models when some covariates (Z) are completely observed but other covariates (X) are missing for some subjects. When data on X are missing at random (i.e., when the probability that X is observed does not depend on the value of X itself), we present a likelihood approach for the observed data that allows the same nuisance parameters to be...

Space-time models with time-dependent covariates

Coping with Nonstationarity in Categorical Time Series

Categorical time series are time sequenced data in which the values at each time point are categories rather than measurements. A categorical time series is considered stationary if the marginal distribution of the data is constant over the time period for which it was gathered and the correlation between successive values is a function only of their distance from each other, and not of their p...

Factorizing Markov Models for Categorical Time Series Prediction

During the last decade, recommender systems became a popular class of models for many commercial websites. One of the best state-of-the-art methods for recommender systems are Matrix and Tensor Factorization models. Besides, Markov Chain models are common for representing sequential data problems (e.g. categorical time series data). The item recommendation problem of recommender systems in fact...

Power Divergence Family of Tests for Categorical Time Series Models

A fundamental issue that arises after fitting a regression model is that of testing the goodness of the fit. Our work brings together the power divergence family of goodness of fit tests and regression models for categorical time series. We show that under some reasonable assumptions, the asymptotic distribution of the power divergence family of goodness of fit tests converges to a normal rando...