Search results for "Expectation–maximization algorithm"
showing 5 items of 25 documents
Maximum likelihood estimation for the exponential power function parameters
1995
This paper addresses the problem of obtaining maximum likelihood estimates for the three parameters of the exponential power function; the information matrix is derived and the covariance matrix is here presented; the regularity conditions which ensure asymptotic normality and efficiency are examined. A numerical investigation is performed for exploring the bias and variance of the maximum likelihood estimates and their dependence on sample size and shape parameter.
Sample Size Requirements of a Mixture Analysis Method with Applications in Systematic Biology
1999
The available information on sample size requirements of mixture analysis methods is insufficient to permit a precise evaluation of the potential problems facing practical applications of mixture analysis. We use results from Monte Carlo simulation to assess the sample size requirements of a simple mixture analysis method under conditions relevant to biological applications of mixture analysis. The mixture model used includes two univariate normal components with equal variances but assumes that the researcher is ignorant as to the equality of the variances. The method used relies on the EM algorithm to compute the maximum likelihood estimates of the mixture parameters, and the likelihood r…
An approximation to maximum likelihood estimates in reduced models
1990
SUMMARY An approximation to the maximum likelihood estimates of the parameters in a model can be obtained from the corresponding estimates and information matrices in an extended model, i.e. a model with additional parameters. The approximation is close provided that the data are consistent with the first model. Applications are described to log linear models for discrete data, to models for multivariate normal distributions with special covariance matrices and to mixed discrete-continuous models.
Affinity Distributions of a Molecularly Imprinted Polymer Calculated Numerically by the Expectation-Maximization Method
2003
Affinity distributions are calculated from adsorption isotherm data obtained for the enantiomers of L- and D-phenylalanine anilide (PA) on native and thermally annealed polymers molecularly imprinted with L-PA. The calculation is obtained with an iterative algorithm called expectation-maximization that does not require prior fit of the data to an isotherm model before inversion and thus yields a distribution indicative of the data only. The results show bimodal distributions, suggestive of a two-site model describing relatively selective and nonselective adsorption modes of the L-enantiomer and a corresponding unimodal/nonselective adsorption mode for the D-enantiomer. The nonselective adso…
Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data
2019
Summary In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixtur…