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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.
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
Length-biased sampling : The bias which arises in the sampling scheme based on the visits of patient, when some individuals are more likely to be chosen than others simply because
The procedure in which the prior distribution is required in the application of Bayesian inference, it is determined from empirical evidence, namely same data for which the posteri
The procedure which targets to use the health and health-related data which precede diagnosis and/or confirmation to identify possible outbreaks of the disease, mobilize a rapid re
Data theory is anxious with how observations are transformed into data which can be analyzed. Data are thus viewed as the theory laden in the sense that the observations can be giv
The Null Hypothesis - H0: Model does not fit the data i.e. all slopes are equal to zero β 1 =β 2 =...=β k = 0 The Alternative Hypothesis - H1: Model does fit the data i.e. at
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
Difference b/w historigram and histogram
Barrett and Marshall Model for conception : A biologically reasonable model for the probability of conception in a particular menstrual cycle, which supposes that the batches of sp
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