A general framework for maximizing likelihood under incomplete data
نویسندگان
چکیده
منابع مشابه
Pseudo-likelihood Estimation for Incomplete Data
In statistical practice, incomplete measurement sequences are the rule rather than the exception. Fortunately, in a large variety of settings, the stochastic mechanism governing the incompleteness can be ignored without hampering inferences about the measurement process. While ignorability only requires the relatively general missing at random assumption for likelihood and Bayesian inferences, ...
متن کاملSpectral subtraction in likelihood-maximizing framework for robust speech recognition
Spectral Subtraction (SS), as a speech enhancement technique, originally designed for improving quality of speech signal judged by human listeners. it usually improve the quality and intelligibility of speech signals, while the speech recognition systems need compensation techniques capable of reducing the mismatch between the noisy speech features and the clean models. This paper proposes a no...
متن کاملA general framework for cooperation under uncertainty
In this paper, we introduce a general framework for situations with decision making under uncertainty and cooperation possibilities. This framework is based upon a two stage stochastic programming approach. We show that under relatively mild assumptions the cooperative games associated with these situations are totally balanced and, hence, have non-empty cores. Finally, we consider several exam...
متن کاملGmm and Empirical Likelihood with Incomplete Data
In applied work economists often encounter data generating mechanisms that produce censored or truncated observations. These dgp’s induce a probability distribution on the realized observations that differs from the underlying distribution for which inference is to be made. If this dichotomy between the target and realized populations is not taken into account, statistical inference can be seve...
متن کاملA general maximum likelihood framework for modulation classification
This paper deals with modulation classification, First, a state of the art is given which is separated into two classes: the pattern recognition approach and the Maximum Likelihood (ML) approach. Then we propose a new classifier called the General Maximum Likelihood Classilier (GMLC) based on an approximation of the likelihood function. We derive equations of this classifier in the case of line...
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: International Journal of Approximate Reasoning
سال: 2018
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2017.10.030