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Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric maximum likelihood is a multinomial likelihood on a sample. Simple examples comprise the empirical cumulative distribution function and the product-limit estimator. It is also used to relax the parametric assumptions regarding random effects in the multilevel models. It is losely related to the empirical likelihood.
Confounding: A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d
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Bayesian network : It is essentially an expert system in which the uncertainty is dealt with using the conditional probabilities and Bayes' Theorem. Formally such type of network c
Chernoff's faces : A method or technique for representing the multivariate data graphically. Each observation is represented by the computer-created face, the features of which are
Command-Line options Compression: C++: ./compress -f myfile.txt [-o myfile.hzip -s Java: sh compress.sh -f myfile.txt [-o myfile.hzip -s] Decompression:
Explain the impact of globalisationon HRM
There are two periods. You observe that Jack consumes 100 apples in period t = 0, and 120 apples in period t = 1. That is, (c 0 ; c 1 ) = (100; 120) Suppose Jack has the util
Window estimates is a term which occurs in the context of the both frequency domain and time domain estimation for the time series. In the previous it generally applies to weights
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
K-means cluster analysis is the method of cluster analysis in which from an initial partition of observations into K clusters, each observation in turn is analysed and reassigned,
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