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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
Johnson''s Job Sequencing for n jobs and 2 machines
A construction for events that happen in some planar area a, consisting of the series of 'territories' each of which comprises of that part of a closer to the particular event xi t
The theorem relating structure of the likelihood to the concept of the sufficient statistic. Officially the necessary and sufficient condition which a statistic S be sufficient for
Atomistic fallacy : A fallacy which arises because of the association between two variables at the individual level might vary from the association between the same two variables m
The process of providing the numerical value for the population parameter on the basis of information gathered from a sample. If a single ?gure is computed for the unknown paramete
The approach of controlling the error rate in an exploratory analysis where number of hypotheses are tested, but where the strict control which is provided by multiple comparison p
Literature controls : The patients with the disease of interest who have received, in the past, one of two treatments under the investigation, and for whom the results have been pu
This is an approach to the modelling of time-frequency surfaces which consists of a Bayesian regularization scheme in which the prior distributions over the time-frequency coeffici
Probability distribution : For the discrete random variable, a mathematical formula which provides the probability of each value of variable. See, for instance, binomial distributi
Median absolute deviation (MAD) : It is the very robust estimator of the scale given by the following equation or, in other words we can say that, the median of the absolute
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