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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.
wat iz z difference b/n logistic regression and multiple regression analysis /
Back-projection: A term most often applied to the procedure for reconstructing plausible HIV incidence curves from the AIDS incidence data. The method or technique assumes that th
The values assigned to factors for the individual sample units in a factor analysis. The most common approach is "regression method". When the factors are seen as the random variab
The regression analysis is used to fit a model describing the relationship of a dependent variable with independent variable(s). Here we have fitted three regression models:
Average age at death : A ?awed statistic summarizing expectancy of the life and other aspects of the mortality. For instance, a study comparing average age at the death for male sy
Modern hotels and certain establishments make use of an electronic door lock system. To open a door an electronic card is inserted into a slot. A green light indicates that the doo
Non-response is the term generally used for the failure to give the relevant information being collected in the survey. Poor response can be because of the variety of causes, for
Quantalassay: The experiment in which the groups of subjects are exposed to the different doses of, generally, a drug, to which the particular number respond. Data from such type
ACC – A pioneer in the Indian cement industry Associated Cement Companies Ltd. (ACC) came into existence in 1936, after the merger of 10 companies belonging to four important bus
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
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