In this paper we discuss the analysis of multi-phase, or multi-stage, case-control studies and present an efficient semiparametric maximum-likelihood approach that unifies and extends earlier work, including the seminal case-control paper by Prentice & Pyke (1979) as well as work by Breslow & Cain (1988), Scott & Wild (1997), Breslow & Holubkov (1997), and others. The theoretical derivations ap...

The lack of readily-available large corpora of aligned monolingual sentence pairs is a major obstacle to the development of Statistical Machine Translation-based paraphrase models. In this paper, we describe the use of annotated datasets and Support Vector Machines to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters found on the World Wide Web. Features inc...

Analogic is a class of analog statistical signal processing circuits that dynamically solve an associated inference problem by locally propagating probabilities in a messagepassing algorithm [29] [15]. In this thesis, we study an exemplary embodiment of analogic called Noise-Locked Loop(NLL) which is a pseudo-random code estimation system. The previous work shows NLL can perform direct-sequence...

Flexible and reliable probability density estimation is fundamental in unsupervised learning and classification. Finite Gaussian mixture models are commonly used to serve this purpose. However, they fail to estimate unknown probability density functions when used for nonparametric probability density estimation, as severe numerical difficulties may occur when the number of components increases....

The error bound in probability between the approximate maximum likelihood estimator AMLE and the continuous maximum likelihood estimator MLE is investigated for nonlinear nonhomogenous stochastic system with unknown parameter. The rates of convergence of the approximations for Itô and ordinary integral are introduced under some regular assumptions. Based on these results, the in probability rat...

This paper deals with the estimation of the stress-strength parameter R = P (Y < X), when X and Y are two independent weighted Lindley random variables with a common shape parameter. The MLEs can be obtained by maximizing the profile loglikelihood function in one dimension. The asymptotic distribution of the MLEs are also obtained, and they have been used to construct the asymptotic confidence ...

This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models (LFMs). FAB inference has not been applicable to models, including LFMs, without a specific condition on the Hessian matrix of a complete loglikelihood, which is required to derive a “factorized information criterion” (FIC). Our asymptotic analysis of the Hessian matrix of LFMs shows that FIC of LFMs has...

The paper deals with a generalized linear model with functional data using a wavelet representation of the signals. A reduction of dimension is first obtained through a principal component analysis. The discriminative function is then given by a loglikelihood maximization, with a LASSO penalization, in order to ensure the sparsity of the wavelet representation. In order to have a data-driven pr...