parametric statistics

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parametric statistics

inferential statistics that assume that the population from which the SAMPLE has been drawn has a particular form, i.e. they involve hypotheses about population parameters. These assumptions are generally that the populations involved have a NORMAL DISTRIBUTION, that they have equal variances (see MEASURES OF DISPERSION) and that the data are at interval level (see CRITERIA AND LEVELS OF MEASUREMENT). Examples are the PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT, multiple regression, and analysis of variance. Such procedures use all available information and tests are more powerful than nonparametric tests. In sociology, the problem of data that are not normally distributed in the population frequently arises. A transformation of scale, a reliance on the robustness of the technique, or a move to a nonparametric equivalent are the available solutions. Compare NONPARAMETRIC STATISTICS.
References in periodicals archive ?
Prediction performance of the linear regression model R5 and 2 neural network models were compared to determine a model with reasonable accuracy for parametric estimation of urban railway project costs.
If the Kristrom's method is compared with the linear parametric estimation the differences are not significant base on the overlapping of the confident intervals.
Distributions other than the gaussian can be used for parametric estimation of reference intervals; an analysis of the TSH data given in the Results assumes that the data are sampled from an exponential distribution.
They are the cornerstones of the parametric estimation technique developed by the RAND Corporation in the 1950s to predict the cost of aircraft.
Recently, [13] considered parametric estimation of the cumulative incidence function (CIF) for competing risks data subject to interval censoring.
The topics include fractional Brownian motion and related processes, parametric estimation for fractional Ornstein-Uhlenbeck type processes, sequential inference and non-parametric inference for processes driven by fractional Brownian motion, parametric estimation for processes driven by a fractional Browning sheet, and self-similarity index estimation.
Table 2 Dichotomous Choice with Certainty Corrections Cert [greater than Model Uncorrected or equal to] 5 Parametric estimation (logit) "Constant" 0.
Although this exclusion restriction is typically not necessary for parametric estimation, it is generally crucial for semi-parametric procedures.

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