Methods for the analysis of unmatched case-control data based on a finite population sampling model are developed. Under this model, and the prospective logistic model for disease probabilities, a likelihood for case-control data that accommodates very general sampling of controls is derived. This likelihood has the form of a weighted conditional logistic likelihood. The flexibility of the meth...

background: several studies have demonstrated an increased risk of congenital hypothyroidism (ch) in girls in comparison to boys. objectives: the aim of this study was to determine the potential interactions that are able to change the effect of gender on congenital hypothyroidism. patients and methods: we conducted a matched 1:4 case-control study in hamadan province, western of iran, from 200...

A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The equations this contain a multiplicative deterministic component describe long-run movements in and, addition, correlations are deterministically time-varying. Parameters estimated jointly using maximum likelihood. Consistency and asymptotic normality likelihood estimators pr...

We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of con...

We propose a new type of multivariate statistical model that permits non-Gaussian distributions as well as the inclusion of conditional independence assumptions induced by a directed acyclic graph. These models feature a specific factorisation of the likelihood that is based on pair-copula constructions and hence involves only univariate distributions and bivariate copulas, of which some may be...

Extended linear modeling provides a flexible framework for functional estimation problems with multiple covariates. Such problems include ordinary and generalized regression, density and conditional density estimation, hazard regression, spectral density estimation and polychotomous regression. In this paper, we develop a general theory on the rate of convergence of maximum likelihood estimatio...

We present a simplified derivation of the extended Baum-Welch procedure, which shows that it can be used for Maximum Mutual Information (MMI) of a large class of continuous emission density hidden Markov models (HMMs). We use the extended Baum-Welch procedure for discriminative estimation of MLLR-type speaker adaptation transformations. The resulting adaptation procedure, termed Conditional Max...

This paper studies estimation of a conditional moment restriction model using the nonparametric maximum likelihood approach proposed by Gallant and Nychka (1987). Under some sufficient conditions, we show that the estimator of some finite dimensional parameters is asymptotically normally distributed and attains the semiparametric efficiency bound and that the estimator of the density function i...

In this paper, we develop a general strategy for linkage analysis, applicable for arbitrary pedigree structures and genetic models with one major gene, polygenes and shared environmental effects. Extending work of Whittemore (1996), McPeek (1999) and Hossjer (2003d), the efficient score statistic is computed from a conditional likelihood of marker data given phenotypes. The resulting semiparame...

The lattice conditional independence (LCI) model N(C)<) is defined to be the set of all normal distributions N(O, E) on IR I such that for every pair L, M E 1<, XL and xM are conditionally independent given xLnM (ct , [AP] (1993a)). Here C)< is a ring of subsets (hence a distributive lattice) of the finite index set I such that 0, IE ".K, while for K E 1<, XK is the coordinate projection of x E...