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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.
Ordered alternative hypothesis is a hypothesis or assumption which speci?es an order for the set of parameters of interest as an alternative to the equality, rather than simply th
Hazard plotting is based on the hazard function of a distribution, this procedure gives estimates of distribution parameters, the proportion of units failing by the given time per
Geographical analysis machine is the procedure designed to detect the clusters of rare diseases in a particular area. Circles of fixed radii are created at each point of the squar
The term which is used in the industrial experimentation, where there is commonly a large set of candidate factors believed to have the possible significant influence on the respon
How large would the sample need to be if we are to pick a 95% confidence level sample: (i) From a population of 70; (ii) From a population of 450; (iii) From a population of 1000;
Mean-range plot is the graphical tool or device useful in selecting a transformation in the time series analysis. The range is plotted against the mean for each of the seasona
Line-intersect sampling is a technique of unequal probability sampling for selecting the sampling units in the geographical area. A sample of lines is drawn in a study area and, w
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 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:
The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional m
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