Analyzing multi-layered graphical models provides insight into understanding the conditional relationships among nodes within layers after adjusting for and quantifying the effects of nodes from other layers. We obtain the penalized maximum likelihood estimator for Gaussian multi-layered graphical models, based on a computational approach involving screening of variables, iterative estimation o...

We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed effects panel models. In its simplest form, the half-panel jackknife, the estimator is just 2θ̂ − θ1/2, where θ̂ is the MLE from the full panel and θ1/2 is the average of the two half-panel MLEs, each using T/2 time periods and all N cross-sectional units. This estimator eliminates...

Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of compone...

This paper proposes a profile likelihood algorithm to compute the nonparametric maximum likelihood estimator of a convex hazard function. The maximisation is performed in two steps: First the support reduction algorithm is used to maximise the likelihood over all hazard functions with a given point of minimum (or antimode). Then it is shown that the profile (or partially maximised) likelihood i...

This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logari...

Loss tomography has received considerable attention in recent years and a large number of estimators based on maximum likelihood (ML) or Bayesian principles have been proposed for the tree topology. In contrast, there has been no maximum likelihood estimator (MLE) proposed for the general topology although there has been enormous interest to extend the estimators proposed for the tree topology ...

In this paper, we show how to use Bayesian approach in the multiplicative heteroscedasticity model proposed by Harvey (1976), where the Gibbs sampler and the Metropolis-Hastings (MH) algorithm are applied. Some candidate-generating densities are considered in our Metropolis-Hastings algorithm. We carry out Monte Carlo study to examine the properties of the estimates via Bayesian approach and it...

We calculate the bias of the profile score for the autoregressive parameters ρ and covariate slopes β in the linear model for N × T panel data with p lags of the dependent variable, exogenous covariates, fixed effects, and unrestricted initial observations. The bias is a vector of multivariate polynomials in ρ with coefficients that depend only on T . We center the profile score and, on integra...

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of pa...