Maximum Likelihood Estimation of Functionals of Discrete Distributions
نویسندگان
چکیده
منابع مشابه
Maximum Likelihood Estimation of Feature-Based Distributions
Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention t...
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 کاملEfficient maximum likelihood estimation of copula based meta t-distributions
Recently an efficient fixed point algorithm for finding maximum likelihood estimates has found its application in models based on Gaussian copulas. It requires a decomposition of a likelihood function into two parts and their iterative maximization. Therefore, this algorithm is called maximization by parts (MBP). For copula-based models, the algorithm MBP improves the efficiency of a two-step e...
متن کاملQuantile maximum likelihood estimation of response time distributions.
We introduce and evaluate via a Monte Carlo study a robust new estimation technique that fits distribution functions to grouped response time (RT) data, where the grouping is determined by sample quantiles. The new estimator, quantile maximum likelihood (QML), is more efficient and less biased than the best alternative estimation technique when fitting the commonly used ex-Gaussian distribution...
متن کاملMaximum likelihood characterization of distributions
Gauss’ principle states that the maximum likelihood estimator of the parameter in a location family is the sample mean for all samples of all sample sizes if and only if the family is Gaussian. There exist many extensions of this result in diverse directions. In this paper we propose a unified treatment of this literature. In doing so we define the fundamental concept of minimal necessary sampl...
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: IEEE Transactions on Information Theory
سال: 2017
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2017.2733537