Given two sequences S1, S2, and a constrained sequence C, a longest common subsequence of S1, S2 with restriction to C is called a constrained longest common subsequence of S1 and S2 with C. At the same time, an optimal alignment of S1, S2 with restriction to C is called a constrained pairwise sequence alignment of S1 and S2 with C. Previous algorithms have shown that the constrained longest co...

Pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and tted both in an exploratory and con rmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and three-stage limited information estimation methods, namely the robust unweighted least squares (3S-RULS) a...

We present a general framework for graph clustering and bi-clustering where we are given a general observation (called a label) between each pair of nodes. This framework allows a rich encoding of various types of pairwise interactions between nodes. We propose a new tractable and robust approach to this problem based on convex optimization and maximum likelihood estimators. We analyze our algo...

Let {Xn, n ≥ 1} be a sequence of pairwise NQD random variables. Some complete convergences and strong laws of large numbers for a weighted sums sequence of pairwise NQD random variables are obtained. The results obtainted generalize the results of Cabrera and Volodin (see [3]).

Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwis...

This paper proposes a new model for the task of word recognition in natural images that simultaneously models visual and lexicon consistency of words in a single probabilistic model. Our approach combines local likelihood and pairwise positional consistency priors with higher order priors that enforce consistency of characters (lexicon) and their attributes (font and colour). Unlike traditional...

Spatial-clustered data refer to high-dimensional correlated measurements collected from units or subjects that are spatially clustered. Such data arise frequently from studies in social and health sciences. We propose a unified modeling framework, termed as GeoCopula, to characterize both large-scale variation, and small-scale variation for various data types, including continuous data, binary ...

We present a general framework for graph clustering where a label is observed to each pair of nodes. This allows a very rich encoding of various types of pairwise interactions between nodes. We propose a new tractable approach to this problem based on maximum likelihood estimator and convex optimization. We analyze our algorithm under a general generative model, and provide both necessary and s...

A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coeffic...