Model Fit after Pairwise Maximum Likelihood
نویسندگان
چکیده
منابع مشابه
Model Fit after Pairwise Maximum Likelihood
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate respon...
متن کاملMaximum likelihood estimates of pairwise rearrangement distances.
Accurate estimation of evolutionary distances between taxa is important for many phylogenetic reconstruction methods. Distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements. Corresponding corrections for genome rearrangement distances fall into 3 categories: Empirical computational studies, Bayesian/MC...
متن کاملMaximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...
متن کاملEvaluating Quality of Fit in Unbinned Maximum Likelihood Fitting
Abstract The unbinned maximimum likelihood fitting method, used in many current analyses including Belle’s measurements of sin and lifetimes, maximizes the use of available information to obtain the shape of a distribution in the face of limited statistics. However, a significant difficulty is that there has been no method for evaluating goodness-of-fit for the result. We examine some issues su...
متن کاملsFit: a method for background subtraction in maximum likelihood fit
This paper presents a statistical method to subtract background in maximum likelihood fit, without relying on any separate sideband or simulation for background modeling. The method, called sFit, is an extension to the sPlot technique originally developed to reconstruct true distribution for each date component. The sWeights defined for the sPlot technique allow to construct a modified likeliho...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Psychology
سال: 2016
ISSN: 1664-1078
DOI: 10.3389/fpsyg.2016.00528