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Post stratification adjustment: One of the most often used population weighting adjustments used in the complex surveys, in which weights for the elements in a class are multiplied by a factor so that total sum of the weights for the respondents in the class equals the population sum for the class. The technique is broadly used in household surveys to control the weighted sample totals to known population totals for particular demographic subgroups. For instance, in the US National Health Interview Survey, post stratification by sex, age, and race is employed.
A theorem which shows that any counting process may be uniquely decomposed as the sum of a martingale and a predictable, right-continous process called the compensator, assuming ce
Martingale: In the gambling context the term at first referred to a system for recouping losses by doubling the stake after each loss has occured. The modern mathematical concept
Raking adjustments is an alternative to the post stratification adjustments in the complex surveys which ensures that the adjusted weights of the respondents conform to each of th
Machine learning is a term which literally means the ability of a machine to recognize patterns which have occurred repetitively and to improve its performance based on the past
Your first task is to realize two additional data generation functions. Firstly, extend the system to generate random integral numbers based on normal distribution. You need to stu
Bubble plot : A method or technique for displaying the observations which involve three variable values. Two of the variables are used to make a scatter diagram and values of the t
Asymmetric proximity matrices : Proximity matrices in which the non-diagonal elements, in the ith row and jth column and the jth row and ith column, are not essentially equal. Exam
Principal factor analysis is the method of factor analysis which is basically equivalent to a principal components analysis performed on reduced covariance matrix attained by repl
data modelling
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
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