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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.
Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models. This will be very
Opreation research phase
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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
Probability distribution : For the discrete random variable, a mathematical formula which provides the probability of each value of variable. See, for instance, binomial distributi
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Invariant transformations to combine marginal probability functions to form multivariate distributions motivated by the need to enlarge the class of multivariate distributions beyo
The act of combining data from heterogeneous sources with the intent of extracting information that would not be available for any single source in isolation. An example is the com
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
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