Comparison of parametric and nonparametric methods for the analysis and inversion of immittance data: Critique of earlier work. J. Comp. Phys., 157:280-301, 2000.J.R. Macdonald, Comparison of parametric and non
In statistics, parametric and nonparametric methodologies refer to those in which a set of data has a normal vs. a non-normal distribution, respectively. Parametric tests make certain assumptions about a data set; namely, that the data are drawn from a population with a specific (normal) distri...
Parametric methods, here represented by AR, ARX, ARMA and ARMAX, are the main concern, but nonparametric methods are also discussed.doi:10.4173/mic.1996.4.1Skullestad ÅgeNorwegian Society of Automatic ControlModeling, Identification and Control (MIC)...
Non-Parametric Versus Parametric Methods for Testing Means Equality. The Case of Stocks MeansManiatis, Paraschos
Estimating efficiency by cost frontiers--a comparison of parametric and nonparametric methods. Applied Economics Letters 1995;284(2):86-90.Sengupta, J. K. (1995). Estimating efficiency by cost frontiers: a comparison of parametric and nonparametric methods. Applied Economics Letters 2(4), 86-90....
An alternative, but less often used, approach to derive standardized drought indices, is the use of a nonparametric method. Different nonparametric methods have been used in drought literature, including the transformation of plotting positions (PP) to the Standard Normal distribution (Farahmand & Agh...
Nonparametric methods include tests that do not involve population parameters at all, such as testing whether the population is normal. Distribution-free tests generally do make some weak assumptions, such as equality of population variances and/or the distribution, and are of the continuous type. ...
(1991). Dealing with nonnormal data: Parametric analysis of transformed data vs nonparametric analysis. Educational & Psychological Measurement, 51, 809-820. Raudenbush, S. W. Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, ...
What is the distinction between distribution-free methods and nonparametric methods? Show that Y(t) = (W(t) - t)^2 is not a Markov process. Define the deterministic model and the probabilistic model. Define and provide an example for the following design method: Stratified sampling. Expla...
Data-driven machine learning methods, such as radial basis function network (RBFN), require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data, particularly when measurements are sparse. Conventional RBFN, however, is direction...