Linear Estimating Equations for Exponential Families with Application to Gaussian Linear Concentration Models

In many families of distributions, maximum likelihood estimation is intractable because the normalization constant for the density which enters into the likelihood function is not easily available. The score matching estimator of Hyv\"arinen (2005) provides an alternative where this normalization constant is not required. The corresponding estimating equations become linear for an exponential family. The score matching estimator is shown to be consistent and asymptotically normally distributed for such models, although not necessarily efficient. Gaussian linear concentration models are examples of such families. For linear concentration models that are also linear in the covariance we show that the score matching estimator is identical to the maximum likelihood estimator, hence in such cases it is also efficient. Gaussian graphical models and graphical models with symmetries form particularly interesting subclasses of linear concentration models and we investigate the potential use of the score matching estimator for this case

Fingerprint Analysis with Marked Point Processes

We present a framework for fingerprint matching based on marked point process models. An efficient Monte Carlo algorithm is developed to calculate the marginal likelihood ratio for the hypothesis that two observed prints originate from the same finger against the hypothesis that they originate from different fingers. Our model achieves good performance on an NIST-FBI fingerprint database of 258 matched fingerprint pairs

Exercise training induces depot-specific adaptations to white and brown adipose tissue

Exercise affects whole-body metabolism through adaptations to various tissues, including adipose tissue (AT). Recent studies investigated exercise-induced adaptations to AT, focusing on inguinal white adipose tissue (WAT), perigonadal WAT, and interscapular brown adipose tissue (iBAT). Although these AT depots play important roles in metabolism, they account for only ∼50% of the AT mass in a mouse. Here, we investigated the effects of 3 weeks of exercise training on all 14 AT depots. Exercise induced depot-specific effects in genes involved in mitochondrial activity, glucose metabolism, and fatty acid uptake and oxidation in each adipose tissue (AT) depot. These data demonstrate that exercise training results in unique responses in each AT depot; identifying the depot-specific adaptations to AT in response to exercise is essential to determine how AT contributes to the overall beneficial effect of exercise11425439This work was supported by National Institutes of Health grants R01-HL138738 and K01-DK105109 (to K.I.S.), R01-DK099511 (to L.J.G.), and 5P30 DK36836 (Joslin Diabetes Center DRC). The authors thank Nathan Makarewicz for editorial contribution

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries

<p>Abstract</p> <p>Background</p> <p>The joint analysis of several categorical variables is a common task in many areas of biology, and is becoming central to systems biology investigations whose goal is to identify potentially complex interaction among variables belonging to a network. Interactions of arbitrary complexity are traditionally modeled in statistics by log-linear models. It is challenging to extend these to the high dimensional and potentially sparse data arising in computational biology. An important example, which provides the motivation for this article, is the analysis of so-called full-length cDNA libraries of alternatively spliced genes, where we investigate relationships among the presence of various exons in transcript species.</p> <p>Results</p> <p>We develop methods to perform model selection and parameter estimation in log-linear models for the analysis of sparse contingency tables, to study the interaction of two or more factors. Maximum Likelihood estimation of log-linear model coefficients might not be appropriate because of the presence of zeros in the table's cells, and new methods are required. We propose a computationally efficient ℓ₁-penalization approach extending the Lasso algorithm to this context, and compare it to other procedures in a simulation study. We then illustrate these algorithms on contingency tables arising from full-length cDNA libraries.</p> <p>Conclusion</p> <p>We propose regularization methods that can be used successfully to detect complex interaction patterns among categorical variables in a broad range of biological problems involving categorical variables.</p

Missing values: sparse inverse covariance estimation and an extension to sparse regression

We propose an l1-regularized likelihood method for estimating the inverse covariance matrix in the high-dimensional multivariate normal model in presence of missing data. Our method is based on the assumption that the data are missing at random (MAR) which entails also the completely missing at random case. The implementation of the method is non-trivial as the observed negative log-likelihood generally is a complicated and non-convex function. We propose an efficient EM algorithm for optimization with provable numerical convergence properties. Furthermore, we extend the methodology to handle missing values in a sparse regression context. We demonstrate both methods on simulated and real data.Comment: The final publication is available at http://www.springerlink.co

Pairing in nuclear systems: from neutron stars to finite nuclei

We discuss several pairing-related phenomena in nuclear systems, ranging from superfluidity in neutron stars to the gradual breaking of pairs in finite nuclei. We focus on the links between many-body pairing as it evolves from the underlying nucleon-nucleon interaction and the eventual experimental and theoretical manifestations of superfluidity in infinite nuclear matter and of pairing in finite nuclei. We analyse the nature of pair correlations in nuclei and their potential impact on nuclear structure experiments. We also describe recent experimental evidence that points to a relation between pairing and phase transitions (or transformations) in finite nuclear systems. Finally, we discuss recent investigations of ground-state properties of random two-body interactions where pairing plays little role although the interactions yield interesting nuclear properties such as 0+ ground states in even-even nuclei.Comment: 74 pages, 33 figs, uses revtex4. Submitted to Reviews of Modern Physic