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A unified approach to all problems of prediction, estimation, and hypothesis testing. It is based on concept of the decision function, which tells the performer of experiment how to conduct the statistical aspects of an experiment and which action to take for each possible outcome. Choosing the decision function needs a loss function to be defined which assigns numerical values to making bad or good decisions. Explicitly a general loss function is denoted by L d; expressing how bad it would be to make decision d if the parameter value were. A quadratic loss function, it could be defined as and a bilinear loss function as
Naor's distribution is the discrete probability distribution which arises from the following model; Assume an urn contains n balls of which one is red and the remainder is whit
Hurdle Model: The model for count data which postulates two processes, one generating the zeros in the data and one generating positive values. The binomial model decides the bina
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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
Knox's tests: These tests designed to detect any tendency for the patients with a particular disease to form the disease cluster in time and space. The tests are relied on a two-b
Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and
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
Infant mortality rate is the ratio of the number of deaths during the calendar year among the infants under one year of age to the total number of live births during that particul
You have probably noticed by now that some of the statements of necessary and sufficient conditions sound more natural than others. For example it seems more natural to express "We
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