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Longitudinal data: The data arising when each of the number of subjects or patients give rise to the vector of measurements representing same variable observed at the number of different time instants.
This type of data combines elements of the multivariate data and time series data. They differ from the previous, however, in that only a single variable is involved, and from the latter in consisting of a large number of short series, one from the each subject, rather than single long series. This kind of data can be collected either prospectively, following subjects forward in time, or the retrospectively, by extracting measurements on each person from historical records. This kind of data is also often called as repeated measures data, specifically in the social and behavioural sciences, though in these disciplines such data are more likely to occur from observing individuals repeatedly under different experimental conditions rather than from a simple time sequence. Special statistical techniques are often required for the analysis of this type of data because the set of measurements on one subject tend to be intercorrelated. This correlation should be taken into account to draw the valid scientific inferences. The design of most of the studies specifies that all the subjects are to have the same number of the repeated measurements made at the equivalent time intervals. Such data is usually referred to as the balanced longitudinal data. But though the balanced data is generally the target, unbalanced longitudinal data in which subjects might have different numbers of repeated measurements made at the differing time intervals, do arise for the variety of reasons. Sometimes the data are unbalanced or incomplete by the design; an investigator might, for instance, choose in advance to take the measurements every hour on one half of the subjects and every two hours on other half.
In general, though, the major reason for the unbalanced data in a longitudinal study is occurrence of missing values in the sense that the intended measurements are not taken, are lost or are otherwise not available.
Graphical deception : Statistical graphics which are not as honest as they should be. It is relatively simple. To mislead the unwary with the graphical material. For instance, c
ain why the simulated result doesn''t have to be exact as the theoretical calculation
Ignorability : The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators
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Indirect least squares: An estimation technique used in the fitting of structural equation models. Commonly least squares are first used to estimate reduced form parameters. Usi
Command-Line options Compression: C++: ./compress -f myfile.txt [-o myfile.hzip -s Java: sh compress.sh -f myfile.txt [-o myfile.hzip -s] Decompression:
Continuous variable : The measurement which is not restricted to the particular values except in so far as this is constrained by the accuracy of measuring instrument. General exam
Geographical analysis machine is the procedure designed to detect the clusters of rare diseases in a particular area. Circles of fixed radii are created at each point of the squar
A name sometimes given to the type of diagram generally used in meta-analysis, in which point estimates and confidence intervals are displayed for all the studies included in the a
Perturbation theory : The theory useful in assessing how well a specific algorithm or the statistical model performs when the observations suffer less random changes. In very commo
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