We compute the maximum likelihood estimates of a principal component analysis on the logit or probit scalem using a majorization algorithm that computes a sequence of singular value decompositions. The technique is applied to 2001 house and senate roll call data and compared with other techniques for roll call analysis.

The maximum likelihood estimates of a principal component analysis on the logit or probit scale are computed using majorization algorithms that iterate a sequence of weighted or unweighted singular value decompositions. The relation with similar methods in item response theory, roll call analysis, and binary choice analysis is discussed. The technique is applied to 2001 US House roll call data.

Researchers typically analyze time-series–cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be misle...

Although a lot of work has been devoted to developing crash severity models to predict the probabilities of crashes for different severity levels, very few studies have considered the underreporting issue in the modeling process. Inferences about a population of interest will be biased if crash data are treated as a random sample coming from the population without considering the different unre...

The multinomial probit (MNP) model is a primary application for combining simulation with estimation. Indeed, McFadden (1989) featured the MNP model in his seminal paper. As random utility model, the MNP model offers a highly desirable flexibility in substitution among alternatives that its chief rival, the multinomial logit model, fails to possess. The unrestricted character of the variance ma...

In this study, we propose two models for predicting people's activity. The first model is the pedestrian distribution prediction (or postdiction) model by multiple regression analysis using space syntax indices of urban fabric and people distribution data obtained from a field survey. The second model is a street choice model for visitors using multinomial logit model. We performed a questionna...

The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive computational mistakes. In this article, I review a menu options interpret results logistic regressions correctly effectively using Stata. consider marginal effects, partial (contrasts of) predictive margins, elasticities, odds risk ratios. also show that interaction term...

In this paper we provide asymptotic theory of local maximum likelihood techniques for estimating a regression model where some regressors are discrete. Our methodology and theory are particularly useful for models that give us a likelihood of the unknown functions we can use to identify and estimate the underlying model. This is the case when the conditional density of the variable of interest,...

Two issues that have become increasingly important while estimating the parameters of aggregate demand functions to study firm behavior are the endogeneity of marketing activities (typically, price) and heterogeneity across consumers in the market under consideration. Ignoring these issues in the estimation of the demand function parameters can lead to biased and inconsistent estimates for the ...