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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 the parameter θ is that the likelihood, l(θ; y) can be expressed in the form given below; For instance, if Y1; Y2; ... ; Yn are the independent random variables from the Poisson distribution with the mean of μ, the likelihood is given by the following formula; which can be factorized into the following equation
Randomized response technique : The procedure for collecting the information on sensitive issues by means of the survey, in which an element of chance is introduced as to what quer
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
Computer-assisted interviews : A method or technique of interviewing subjects in which the interviewer reads the question from the computer screen instead of the printed page, and
Bayes factor : A summary of evidence for the modelM1 against the another modelM0 provided by the set of data D, which can be used in the model selection. Given by the ratio of post
Blinding : A procedure used in clinical trials to get rid of the possible bias which might be introduced if the patient and/or the doctor knew which treatment the patient is receiv
Tree is the term from the branch of the mathematics which known as the graph theory, used to describe any set of the straight-line segments joining the pairs of points in some pro
Length-biased data is a data which arise when the probability that an item is sampled is proportional to its own length. A main example of this situation occurs in the renewal the
Dear Experts, Please note that I''m doing a PhD in Business management under the title: Technology transfer and competitive advantage in Qatar oil and gas companies. It is a quant
Historical controls : The group of patients treated in the past with the standard therapy, taken in use as the control group for evaluating the new treatment on the present patient
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
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