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 ?
The misspecification of the traditional parametric model was confirmed further when the same panel data were used to conduct parametric estimations, where the results were found to be in line with the strong EKC hypothesis.
The stability condition of the proposed parametric estimation scheme is proved using the small gain theorem [39] and based on the stability condition of the RLS algorithm [40].
The formulation of the parametric estimation problem for this class of large-scale systems, which is described by the discrete model (5), was proved by Elloumi and Kamoun [12].
Variable Minimum Mean Maximum Std-deviation lnHCE 1.344 3.347 6.365 1.034 lnGDP 4.634 6.344 9.213 1.029 lnPOP65 0.637 1.128 1.936 0.245 lnIMR 1.411 4.238 5.071 0.619 22 lower income Benin; Burkina Faso; Burundi; Central countries African Republic; Chad; Comoros; Congo, Dem.Rep; Eritrea; Ethiopia; Guinea; Guinea-Bissau; Kenya; Madagascar; Malawi; Mali; Mozambique; Niger; Rwanda; Sierra Leone; Tanzania; Togo; Uganda 20 middle Angola; Algeria; Botswana; Cameroon; income Congo, Rep.; Cote d'Ivoire; Djibouti; countries Egypt, Arab Rep.; Ghana; Gabon; Lesotho; Mauritania; Mauritius; Nigeria; Senegal; Sudan; South Africa; Swaziland; Tunisia; Zambia TABLE 2: Parametric estimation results.
Existing PTFs can be divided into two groups: point estimation and parametric estimation (Minasny et al.
In Figure 6 is shown the results of parametric estimation of the return value and its 95% CI for 13 years of hourly maxima.
Bresler, "A compact Cramer-Rao bound expression for parametric estimation of superimposed signals," IEEE Transactions on Signal Processing, Vol.
However, both parametric and semiparametric estimation show economies of scale for the graduate program, except in the science/technology university based on the parametric estimation. TABLE 5 Ray Scale Economies and Product-Specific Scale Economies RES [PSE.sub.U] [PSE.sub.G] All samples Parametric 1.3739 0.9992 1.6742 Semiparametric 0.9955 0.7533 1.3946 Comprehensive university Parametric 1.5017 0.9992 1.9022 Semiparametric 1.0248 0.7575 1.4259 Science/technology university Parametric 0.9694 0.9992 0.9524 Semiparametric 0.9024 0.7399 1.2957 Public university Parametric 1.5800 0.9995 1.2152 Semiparametric 0.9953 0.8017 1.3464 Private university Parametric 1.1799 0.9989 2.1065 Semiparametric 0.9955 0.7077 1.4401
The previous parametric modeling studies mainly focused on parametric estimation of construction costs for building and highway projects.
In a parametric estimation of land prices, the inclusion of variables to represent location within a certain county or distance from an identified "employment subcenter" (such as Chicago O' Hare airport in Colwell and Munneke [1991]) is essential to achieving a reasonably accurate land price surface.
The third method is called parametric estimation; it utilizes some selected product design parameters, which are essential in cost and production time estimation.
Since differences in spectral content allow a visual discrimination of different types of volcanic earthquakes (Zobin, 2003), we have calculated the spectrum for each signal by using two different approaches: (i) N-point Fast Fourier Transform (FFT) and, (ii) parametric estimation of the power spectral density (PSD).

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