A novel method is presented for estimating the parameters of a parametric diffusion process. The approach based on closed-form Maximum Likelihood estimator an approximating Continuous Time Markov Chain (CTMC) Unlike typical time discretization approaches, such as pseudo-likelihood approximations with Shoji-Ozaki or Kessler's method, CTMC approximation introduces no time-discretization error dur...

Delft University of Technology and Vrije Universiteit Amsterdam, University of Washington and University of Washington We study nonparametric estimation for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider the ‘naive estimator’ of Jewell, Van der Laan and Henneman [10]. Groeneboom, Maathui...

We investigate the rate of convergence of the distribution of the maximum likelihood estimator (MLE) of an unknown parameter in the drift coefficient of a stochastic process described by a linear stochastic differential equation driven by a fractional Brownian Motion (fBM). As a special case, we obtain the rate of convergence for the case of the fractional Ornstein-Uhlenbeck type process studie...

We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists of coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any samp...

Maximum Entropy Distributions on Graphs by Andre Yohannes Wibisono Master of Arts in Statistics University of California, Berkeley Professor Michael I. Jordan, Chair We study the maximum entropy distribution on weighted graphs with a given expected degree sequence. This distribution on graphs is characterized by independent edge weights parameterized by vertex potentials at each node. Using the...

BACKGROUND Edwards's method is a widely used approach for fitting a sine curve to a time-series of monthly frequencies. From this fitted curve, estimates of the seasonal intensity of occurrence (i.e., peak-to-low ratio of the fitted curve) can be generated. METHODS We discuss various approaches to the estimation of seasonal intensity assuming Edwards's periodic model, including maximum likeli...

This article concerns the variance estimation in the central limit theorem for finite recurrent Markov chains. The associated variance is calculated in terms of the transition matrix of the Markov chain. We prove the equivalence of different matrix forms representing this variance. The maximum likelihood estimator for this variance is constructed and it is proved that it is strongly consistent ...

This paper tries to explore the asymptotic properties of maximum likelihood estimators for spatial dynamic panel data with fixed effects when both the number of time periods T and number of individuals n are large. When n is proportional to T or T is relatively large, the estimator is √ nT consistent and asymptotically normal; when n is relatively large, the estimator is consistent with the rat...

MOTIVATION The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need...

This paper provides the Bayes estimators of the failure rate and reliability function for a one-parameter, exponential distribution by utilizing a point guess estimate of the parameter. For deriving the Bayes estimators, the prior distributions are chosen such that they are centered at the known prior values of parameters. The validity of proposed estimators is examined with respect to their ma...