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Time series: The values of a variable recorded, generally at a regular interval, over the long period of time.
The observed movement and fluctuations of several such series are composed of four diverse components, seasonal variation, secular trend, cyclical variation, and the irregular variation. An instance from medicine is the incidence of the disease recorded yearly over several decades. Such type of data usually needs special methods for their analysis because of presence of the serial correlation between separate observations. Most often time series are analyzed by the linear models such the classic family of the autoregressive moving average models.
But there are number of observable phenomena which cannot be accounted for adequately by the linear models and which give rise to the nonlinear time series, for which special models have been developed, for instance, autoregressive conditional heteroscedastic models.
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Models which make use of the smoothing techniques such as locally weighted regression to identify and represent the possible non-linear relationships between the explanatory and th
Coefficient of concordance : The coef?cient is taken in use to assess the agreement among m raters ranking n individuals according to some of the speci?c characteristic. Which can
A rule for computing the number of classes to use while constructing a histogram and can be given by here n is the sample size and ^ γ is the estimate of kurtosis.
The Null Hypothesis - H0: γ 1 = γ 2 = ... = 0 i.e. there is no heteroscedasticity in the model The Alternative Hypothesis - H1: at least one of the γ i 's are not equal
Behrens Fisher problem : The difficulty of testing for the equality of the means of the two normal distributions which do not have the equal variance. Various test statistics have
Comparative exposure rate : A measure of alliance for use in a matched case-control study, de?ned as the ratio of the number of case-control pairs, where the case has greater expos
re-reference all these indexes
1) Let N1(t) and N2(t) be independent Poisson processes with rates, ?1 and ?2, respectively. Let N (t) = N1(t) + N2(t). a) What is the distribution of the time till the next epoch
Invariant transformations to combine marginal probability functions to form multivariate distributions motivated by the need to enlarge the class of multivariate distributions beyo
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