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Topic: Nonparametric

	Non-parametric statistics - Wikipedia, the free encyclopedia
		Nonparametric models differ from parametric models in that the model structure is not specified a priori, but is instead determined from data.
		The term nonparametric is not meant to imply that such models completely lack parameters; rather, the number and nature of the parameters is flexible and not fixed in advance.
		Nonparametric tests have less power than the appropriate parametric tests, but are more robust when the assumptions underlying the parametric test are not satisfied.
	en.wikipedia.org /wiki/Nonparametric (216 words)

	RESEARCH FORUM--Nonparametric Statistics: Methods for Analyzing Data Not Meeting Assumptions Required for the ...
		Nonparametric tests can be used with data that are of the nominative (e.g., characteristics such as right and left, male and female) and ordinal (e.g., mild, moderate and severe) levels of measurement, which may not follow the normal distribution curve or comply with other assumptions required of data analyzed by parametric statistical methods.
		Nonparametric tests sometimes are referred to as "distribution-free." That is, the data can be drawn from a sample that may not follow the normal distribution (1).
		Nonparametric tests should be used when the assumptions of parametric tests cannot be met, when very small numbers of data are used, and when no basis exists for assuming certain types or shapes of distributions (9).
	www.oandp.org /jpo/library/1996_03_105.asp (5206 words)

	Nonparametric Statistics
		In more technical terms, nonparametric methods do not rely on the estimation of parameters (such as the mean or the standard deviation) describing the distribution of the variable of interest in the population.
		Nonparametric alternatives to this method are Friedman's two-way analysis of variance and Cochran Q test (if the variable was measured in terms of categories, e.g., "passed" vs. "failed").
		On the other hand, nonparametric statistics are less statistically powerful (sensitive) than their parametric counterparts, and if it is important to detect even small effects (e.g., is this food additive harmful to people?) one should be very careful in the choice of a test statistic.
	www.statsoft.com /textbook/stnonpar.html (1917 words)

	Nonparametric Life Data Analysis
		Nonparametric analysis allows the user to analyze data without assuming an underlying distribution.
		On the other hand, the confidence bounds associated with nonparametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible.
		There are several methods for conducting a nonparametric analysis, including the Kaplan-Meier, simple actuarial, and standard actuarial methods.
	www.weibull.com /LifeDataWeb/nonparametric_analysis.htm (189 words)

	Nonparametric Statistics
		One answer is given by the asymptotic relative efficiency (ARE) which, loosely speaking, describes the ratio of sample sizes required (parametric to nonparametric) for a parametric procedure to have the same ability to reject a null hypothesis as the corresponding nonparametric procedure.
		Roughly speaking, a nonparametric test is test one which makes no hypothesis about the value of a parameter in a statistical density function, whereas a distribution-free test is one which makes no assumptions about the precise form of the sampled population.
		distribution-free or nonparametric, that is, free of the assumption that [the underlying distribution of the data] belongs to some parametric family of distributions.
	www.tufts.edu /~gdallal/npar.htm (2914 words)

	Statistics Glossary - nonparametric methods
		Nonparametric tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable.
		Nonparametric tests may be, and often are, more powerful in detecting population differences when certain assumptions are not satisfied.
		It is used to test the null hypothesis that two populations have identical distribution functions against the alternative hypothesis that the two distribution functions differ only with respect to location (median), if at all.
	www.stats.gla.ac.uk /steps/glossary/nonparametric.html (744 words)

	How to choose a statistical test
		Since the nonparametric test only knows about the relative ranks of the values, it won't matter that you didn't know all the values exactly.
		When you use a nonparametric test with data from a Gaussian population, the P values tend to be too high.
		The nonparametric tests are not powerful and the parametric tests are not robust.
	www.graphpad.com /www/Book/Choose.htm (1912 words)

	Nonparametric Regression (Site not responding. Last check: 2007-11-03)
		Nonparametric regression relaxes the usual assumption of linearity and enables you to explore the data more flexibly, uncovering structure in the data that might otherwise be missed.
		However, many forms of nonparametric regression do not perform well when the number of independent variables in the model is large.
		The problem of rapidly increasing variance for increasing dimensionality is sometimes referred to as the "curse of dimensionality." Interpretability is another problem with nonparametric regression based on kernel and smoothing spline estimates.
	support.sas.com /rnd/app/da/new/802ce/stat/chap5/sect13.htm (290 words)

	Nonparametric Statistics
		Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution.
		Another important reason for using these tests is that they allow for the analysis of categorical as well as rank data.
		Since nonparametric tests require fewer assumptions and can be used with a broader range of data types, the question becomes, "Why not use them all of the time?" Parametric tests are often preferred because:
	www.uwsp.edu /psych/stat/14/nonparm.htm (719 words)

	Nonparametric: Distribution-Free, Not Assumption-Free
		Depending on the particular procedure, nonparametric methods may be almost as powerful as the corresponding parametric procedure when the assumptions of the latter are met.
		While nonparametric methods require no assumptions about the population probability distribution functions, they are based on some of the same assumptions as parametric methods, such as randomness and independence of the samples.
		In addition, many nonparametric tests are sensitive to the shape of the populations from which the samples are drawn.
	www.isixsigma.com /library/content/c050314a.asp (744 words)

	Nonparametric Modeling
		The KDE procedure computes nonparametric estimates of univariate and bivariate probability density functions using the method of kernel density estimation.
		The LOESS procedure implements a nonparametric method for estimating local regression surfaces that allows great flexibility because it requires no assumptions about the parametric form of the regression surface.
		The LOESS procedure fits nonparametric models and supports the use of multidimensional data, multiple dependent variables, and both direct and interpolated fitting using kd trees.
	support.sas.com /rnd/app/da/new/danpm.html (531 words)

	Amazon.com: Nonparametric Econometrics: Books: Adrian Pagan,Aman Ullah,Peter C. B. Phillips,Eric Ghysels,Richard J. ... (Site not responding. Last check: 2007-11-03)
		Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models.
		Nonparametrics seems to be one of the most promising fields in econometrics.
		In the introduction there is a clear explanation of the difference between parametrics and nonparametrics; you will also learn the main basic methods and concepts, such as the nearest Neighborhood Estimator and the window's size problem.
	www.amazon.com /exec/obidos/tg/detail/-/0521586119?v=glance (1522 words)

	Wiley::Nonparametric Analysis of Longitudinal Data in Factorial Experiments
		Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach.
		Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points.
		Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics.
	www.wiley.com /WileyCDA/WileyTitle/productCd-047144166X.html (357 words)

	Applied Nonparametric Econometrics (Site not responding. Last check: 2007-11-03)
		The primary objective is to enable students in economics to apply nonparametric methods to relevant economic issues.
		Particular attention will be given to those nonparametric techniques that are best suited to relax unnecessary econometric restrictions not imposed by economic theory and to deal with typical problems arising in the analysis of economic data (boundedness, non-stationarity, etc.).
		The objective of the final project is to reproduce a relevant nonparametric econometric application taken from a published paper to solve a different economic problem from the original one or to apply it to
	www-users.york.ac.uk /~jb35/ane.htm (1470 words)

	Two Groups: Nonparametric Tests (Site not responding. Last check: 2007-11-03)
		This set of notes looks at nonparametric tests of differences between two groups.
		Nonparametric tests should be used when the dependent variable is ordinal or when the t test assumptions have not been met.
		The decision tree presented in the previous set of notes is reproduced here.
	web.uccs.edu /lbecker/spss80/nonpar.htm (1703 words)

	Wiley::Nonparametric Statistical Methods, 2nd Edition
		The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s.
		This long-awaited Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area.
		An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems.
	www.wiley.com /WileyCDA/WileyTitle/productCd-0471190454.html (322 words)

	Applied Nonparametric Regression - Cambridge University Press (Site not responding. Last check: 2007-11-03)
		Applied Nonparametric Regression is a very welcome addition to the literature." Journal of the American Statistical Association
		The user-friendly approach taken by the author has successfully smoothed out most of the formidable asymptotic elaboration in developing the theory.
		"This monograph on nonparametric regression presents a particularly clear and balanced view of the methodology and practice of this very important subject, and so is of use to theoreticians and practitioners alike." Peter Hall, University of Glasgow
	www.cambridge.org /us/catalogue/catalogue.asp?isbn=0521382483 (381 words)

	JOEL L. HOROWITZ (Site not responding. Last check: 2007-11-03)
		"Nonparametric Methods for Inference in the Presence of Instrumental Variables," Annals of Statistics,33, 2904-2929, 2005 (with P. Hall).
		"Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data," Journal of the American Statistical Association, 95, 77-84, 2000 (with C.F. Manski).
		Nonparametric Methods for Inference in the Presence of Instrumental Variables, (with P. Hall)
	faculty.econ.northwestern.edu /faculty/horowitz (317 words)

	Second Moment: Resource Links: Nonparametric Statistics
		But what you must never forget is that every one of those figures comes in the first instance from the village watchman, who just puts down what he damn pleases." - Sir Josiah Stamp
		Nonparametric GARCH Models by Peter Buhlmann and Alexander J. McNeil, Dec. 1999
		Extensive list of readings pertaining to nonparametric statistics.
	www.secondmoment.org /nonparametric.php (130 words)

	Nonparametric Statistics
		Overview of nonparametric procedures, when to use which method, nonparametric correlations.
		The objective of this paper is to offer an outlook of some statistical techniques that are being developed in the most recent years and that can be of interest for archaeologists in the short run.
		This applet lets you explore the effect of violations of the assumptions of normality and homogeneity of variance on the type I error rate and power of t tests (and two-group analysis of variance).
	www.mrs.umn.edu /~sungurea/introstat/nonparametric/links.html (167 words)

	SNP: A Program for Nonparametric Time Series Analysis
		SNP is a method of nonparametric time series analysis.
		An appealing feature of this expansion is that it is a nonlinear nonparametric model that directly nests the Gaussian VAR model, the semiparametric VAR model, the Gaussian GARCH model, and the multivariate BEKK GARCH.
		The SNP model is fitted using conventional maximum likelihood together with a model selection strategy that determines the appropriate degree of the polynomial.
	www.econ.duke.edu /~get/snp.html (309 words)

	Nonparametric Methods
		Nonparametric statistical methods provide alternatives to classical analyses without making the usual assumption that data come from a normal distribution (or any other specific distribution).
		Typically, the nonparametric techniques are included as options in the procedures that implement the parametric techniques, rather than being lumped together in a separate menu.
		One Sample Analysis - sign test and signed rank test for location.
	www.statgraphics.com /nonparametric_methods.htm (145 words)

	St@tServ Statistical Online Bookstore - Nonparametric statistics (Site not responding. Last check: 2007-11-03)
		Bootstrapping : A Nonparametric Apporach to Statistical Inference
		Nonparametric Estimation of Probability Densities and Regression Curves
		Nonparametric Regression and Generalized Linear Models : A Roughness Penalty Approach
	www.statserv.com /books/books-nonpar.html (57 words)

	Biostatistics Software (Site not responding. Last check: 2007-11-03)
		The MATLAB Smoothing Toolbox is a shell script of tools for integration of nonparametric regression and smoothing techniques into MATLAB.
		lpridge is a shell script with fortran-code and Splus-interface for nonparametric estimation of regression functions and their derivatives via local polynomials and local polynomial ridge regression with polynomial weight functions.
		It is a realization of the fast and stable algorithm presented in JCGS (1994) with ridging proposed by Seifert and Gasser in JASA (1996).
	www.unizh.ch /biostat/Software (223 words)

	Nonparametric Regression Monographs (Site not responding. Last check: 2007-11-03)
		Nonparametric Simple Regression and Multiple and Generalized Nonparametric Regression can be ordered from Sage Publications.
		Annotated bibliography of selected sources on nonparametric regression
		Please contact me at jfox AT mcmaster.ca with any problems or suggestions.
	socserv.socsci.mcmaster.ca /jfox/Books/Nonparametric-Regression (173 words)

	Keith Price Bibliography Nonparametric Clustering
		Loftsgaarden, D., and Quesenberry, C. A Nonparametric Estimate of a Multivariate Density Function,
		Swain, P.H. On Nonparametric and Linguistic Approaches to Pattern Recognition,
		Tsuda, K. Minoh, M. A Nonparametric Density Model for Classification in a High Dimensional Space,
	iris.usc.edu /Vision-Notes/bibliography/pattern629.html (420 words)

	Section on Nonparametric Statistics
		This page is maintained for the newest section of the American Statistical Association: The Section on Nonparametric Statistics.
		This Section was approved by the Council of Sections on August 8, 1999 and officially began its existence on January 1, 2000.
		Announcements, our officers, our charter and other information will be posted here periodically.
	www.math.niu.edu /NPAR (53 words)

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