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Window estimates is a term which occurs in the context of the both frequency domain and time domain estimation for the time series. In the previous it generally applies to weights frequently applied to improve the accuracy of the periodogram for estimating spectral density. In latter it refers to the statistics calculated from the small subsets of the observations after the data has been splitted up into segments.
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
Interim analyses : An analysis made before the planned end of a clinical trial, typically with the aim of detecting the treatment differences at the early stage and thus preventing
Matching coefficient is a similarity coefficient for data consisting of the number of binary variables which is often used in cluster analysis. It can be given as follows he
Cellular proliferation models : Models are used to describe the growth of the cell populations. One of the example is the deterministic model where N(t) is the number of cel
Chapter 7 2. Describe the distribution of sample means (shape, expected value, and standard error) for samples of n =36 selected from a population with a mean of µ = 100 and a sta
The measure of the degree to which the particular model differs from the saturated model for the data set. Explicitly in terms of the likelihoods of the two models can be defined a
Cointegration : The vector of not motionless time sequence is said to be cointegrated if the linear combination of the individual series is stationary. Facilitates suitable testing
Partial least squares is an alternative to the multiple regressions which, in spite of using the original q explanatory variables directly, constructs the new set of k regressor v
Meta-analysis is the collection of techniques whereby the results of two or more independent studies are statistically combined to yield the overall answer to a question of intere
Grade of membership model: This is the general distribution free method for the clustering of the multivariate data in which only categorical variables are included. The model ass
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