Sherlock, Chris
Published in
Computational statistics

Given noisy, partial observations of a time-homogeneous, finite-statespace Markov chain, conceptually simple, direct statistical inference is available, in theory, via its rate matrix, or infinitesimal generator, Q , since exp ( Q t ) is the transition matrix over time t. However, perhaps because of inadequate tools for matrix exponentiation in pro...

Henderson, Donna Lunter, Gerton
Published in
Computational statistics

Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are intractable, stochastic approximation EM (SAEM) is of...

Li, Lingge Holbrook, Andrew Shahbaba, Babak Baldi, Pierre
Published in
Computational Statistics

Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of complex Bayesian models. It can efficiently explore high-dimensional parameter spaces guided by simulated Hamiltonian flows. However, the algorithm requires repeated gradient calculations, and these computations become increasingly burdensome as data set...

Tang, Lu Zhou, Ling Song, Peter X. K.
Published in
Computational Statistics

We propose a fusion learning procedure to perform regression coefficients clustering in the Cox proportional hazards model when parameters are partially heterogeneous across certain predefined subgroups, such as age groups. One major issue pertains to the fact that the same covariate may have different influence on the survival time across differen...

Cheng, Hao Wei, Ying
Published in
Computational Statistics

In many applications, some covariates could be missing for various reasons. Regression quantiles could be either biased or under-powered when ignoring the missing data. Multiple imputation and EM-based augment approach have been proposed to fully utilize the data with missing covariates for quantile regression. Both methods however are computationa...

Yan, Yanyang Zhang, Feipeng Zhou, Xiaoying
Published in
Computational statistics

This paper considers a new estimating method for the bent line quantile regression model. By a simple linearization technique, the proposed method can simultaneously obtain the estimates of the regression coefficients and the change-point location. Moreover, it can be readily implemented by current software. Simulation studies demonstrate that the ...

Pfeiffer, Ruth M. Redd, Andrew Carroll, Raymond J.
Published in
Computational Statistics

We assessed the ability of several penalized regression methods for linear and logistic models to identify outcome-associated predictors and the impact of predictor selection on parameter inference for practical sample sizes. We studied effect estimates obtained directly from penalized methods (Algorithm 1), or by refitting selected predictors with...

Dinov, Ivo D Siegrist, Kyle Pearl, Dennis K Kalinin, Alexandr Christou, Nicolas
Published in
Computational statistics

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications,...

Hornik, Kurt Grün, Bettina
Published in
Computational statistics

Maximum likelihood estimation of the concentration parameter of von Mises-Fisher distributions involves inverting the ratio [Formula: see text] of modified Bessel functions and computational methods are required to invert these functions using approximative or iterative algorithms. In this paper we use Amos-type bounds for [Formula: see text] to de...

Joo, Yongsung Casella, G Hobert, J
Published in
Computational statistics

Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering m...