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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.
Product-limit estimator is a method for estimating the survival functions for the set of survival times, some of which might be censored observations. The logic behind the procedu
Graphical deception : Statistical graphics which are not as honest as they should be. It is relatively simple. To mislead the unwary with the graphical material. For instance, c
Kurtosis: The extent to which the peak of the unimodal probability distribution or the frequency distribution departs from its shape of the normal distribution, by either being mo
Multivariate data is the data for which each observation consists of the values for more than one random variable. For instance, measurements on the blood pressure, temperature an
Regression through the origin : In some of the situations a relationship between the two variables estimated by the regression analysis is expected to pass by the origin because th
Data theory is anxious with how observations are transformed into data which can be analyzed. Data are thus viewed as the theory laden in the sense that the observations can be giv
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
An unusual aggregation of the health events, real or perceived. The events might be grouped in the particular region or in some short period of time, or they might happen among the
Gllamm is a program which estimates the generalized linear latent and mixed models by the maximum likelihood. The models which can be fitted include structural equation models mul
Observation-driven model is a term generally applied to models for the longitudinal data or time series which introduce within the unit correlation by specifying the conditional
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