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In this work we propose a model-based clustering method for time series. The model uses an almost surely discrete Bayesian nonparametric prior to induce clustering of the series. Specifically we propose a general Poisson-Dirichlet process mixture model, which includes the Dirichlet process mixture model as particular case. The model accounts for typical… (More)

- Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, Luis E Nieto-Barajas, Jeffrey S Morris
- Journal of the American Statistical Association
- 2010

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically… (More)

- Luis E. Nieto-Barajas
- Computational Statistics & Data Analysis
- 2014

- B Nebiyou Bekele, Luis E Nieto-Barajas, Mark F Munsell
- Journal of statistical theory and practice
- 2012

Our goal is to model the joint distribution of a series of 4×2×2×2 contingency tables for which some of the data are partially collapsed (i.e., aggregated in as few as two dimensions). More specifically, the joint distribution of 4 clinical characteristics in breast cancer patients is estimated. These characteristics include estrogen receptor status… (More)

This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic… (More)

- Luis E Nieto-Barajas, Peter Müller, Yuan Ji, Yiling Lu, Gordon B Mills
- Biometrics
- 2012

Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to coregulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers,… (More)

A comparative analysis of time series is not feasible if the observation times are different. Not even a simple dispersion diagram is possible. In this article we propose a Gaussian process model to interpolate an unequally spaced time series and produce predictions for equally spaced observation times. The dependence between two observations is assumed a… (More)

- Luis E Nieto-Barajas, Stephen G Walker
- Lifetime data analysis
- 2005

In the presence of covariate information, the proportional hazards model is one of the most popular models. In this paper, in a Bayesian nonparametric framework, we use a Markov (Lévy-driven) process to model the baseline hazard rate. Previous Bayesian nonparametric models have been based on neutral to the right processes, which have a number of drawbacks,… (More)

- Luis E. Nieto-Barajas, Stephen G. Walker
- Computational Statistics & Data Analysis
- 2007

In this paper we introduce a Bayesian semiparametric model for bivariate and multivariate survival data. The marginal densities are well-known nonparametric survival models and the joint density is constructed via a mixture. Our construction also defines a copula and the properties of this new copula are studied. We also consider the model in the presence… (More)

- Luis E Nieto-Barajas, Peter Müller
- Scandinavian journal of statistics, theory and…
- 2012

Polya trees (PT) are random probability measures which can assign probability 1 to the set of continuous distributions for certain specifications of the hyperparameters. This feature distinguishes the PT from the popular Dirichlet process (DP) model which assigns probability 1 to the set of discrete distributions. However, the PT is not nearly as widely… (More)

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