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...[[order statistics]], which form the basis for [[Nonparametric statistics|nonparametric statistical methods]]. ...cs |accessdate=14 February 2020 |date=7 February 2011}}</ref> The [[Range (statistics)|sample range]] is given by <math>R_n = X_{(n)}-X_{(1)}</math>,<ref name="s ...

In [[statistics]], the '''mean integrated squared error (MISE)''' is used in [[density esti ...nknown density, ''ƒ''_''n'' is its estimate based on a [[sample (statistics)|sample]] of ''n'' [[independent and identically distributed]] random varia ...

{{Short description|Nonparametric test in statistics}} In statistics, the '''Cucconi test''' is a [[nonparametric test]] for jointly comparing [[central tendency]] and [[Statistical variabi ...

...es a one-dimensional [[Smoothing|smoother]] to build a restricted class of nonparametric regression models. Because of this, it is less affected by the [[curse of d ...hirani, R. (1989). "Linear Smoothers and Additive Models", ''The Annals of Statistics'' 17(2):453&ndash;555. {{JSTOR|2241560}}</ref> ...

{{Short description|Nonparametric estimate of cumulative hazard}} ...he [[hazard rate|cumulative hazard rate]] function in case of [[Censoring (statistics)|censored data]] or [[missing data|incomplete data]].<ref>{{cite web |url=h ...

...tion procedures) that is useful in dealing with their [[Asymptotic theory (statistics)|asymptotic behaviour]] as the amount of data increases.<ref>{{cite book |f ...ic equicontinuity is needed in establishing the [[uniform convergence]] of nonparametric estimators. Like - [[Kernel density estimation|kernel density estimators]] ...

*S. N. MacEachern, "Dependent Nonparametric Processes", in ''Proceedings of the Bayesian Statistical Science Section'', [[Category:Nonparametric statistics]] ...

In [[statistics]], '''[[cumulative distribution function]] (CDF)-based nonparametric confidence intervals''' are a general class of [[confidence interval]]s aro ...ke approaches that make asymptotic assumptions, including [[Bootstrapping (statistics)|bootstrap approaches]] and those that rely on the [[central limit theorem] ...

In [[statistics]], '''Hoeffding's test of independence''', named after [[Wassily Hoeffding] .../www.jstor.org/stable/pdf/2237758.pdf|journal=[[The Annals of Mathematical Statistics]]|year=1961 |volume=32|pages=485–498|number=2|doi=10.1214/aoms/1177705055 | ...

In statistics, a '''hidden Markov random field''' is a generalization of a [[hidden Marko ...idered a user-defined constant. However, ideas from nonparametric Bayesian statistics, which allow for data-driven inference of the number of states, have been a ...

In [[probability theory]] and [[statistics]], '''smoothness''' of a [[density function]] is a measure which determines ...convergence for nonparametric deconvolution problems|journal=The Annals of Statistics|volume=19|issue=3|pages=1257–1272|jstor=2241949|doi=10.1214/aos/1176348248| ...

...archical Dirichlet process''' ('''HDP''') is a [[Non-parametric statistics|nonparametric]] [[Bayesian probability|Bayesian]] approach to clustering [[grouped data]] | title = Hierarchical Bayesian Nonparametric Models with Applications ...

In statistics, '''Somers’ ''D''''', sometimes incorrectly referred to as Somer’s ''D'', i ...journal|last1=Newson|first1=Roger|title=Parameters behind "nonparametric" statistics: Kendall's tau, Somers' ''D'' and median differences|journal=Stata Journal| ...

...=763157853}}</ref> Trimmed estimators also often have higher [[Efficiency (statistics)|efficiency]] for [[mixture distribution]]s, and [[heavy-tailed distributio * [[Interquartile range]], the 25% trimmed [[range (statistics)|range]] ...

...ernating Conditional Expectations (ACE)''' is a [[nonparametric statistics|nonparametric]] [[algorithm]] used in [[regression analysis]] to find the optimal transfo In [[statistics]], a nonlinear transformation of variables is commonly used in practice in ...

[[Category:Nonparametric statistics]] [[Category:Bayesian statistics]] ...

...erval estimation]] by eliminating procedures based on [[Asymptotic theory (statistics)|asymptotic]] and approximate statistical methods. The main characteristic ...s that do not make any distributional assumptions are referred to as exact nonparametric methods. The latter has the advantage of making fewer assumptions whereas, ...

...ald Cramér]] in 1946.<ref>Cramér, Harald. 1946. ''Mathematical Methods of Statistics''. Princeton: Princeton University Press, page 282 (Chapter 21. The two-di ...bles<ref name="Ref_a">Sheskin, David J. (1997). Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, Fl: CRC Press.</ref> and may be used wi ...

...://link.springer.com/book/10.1007/0-387-30623-4 |journal=Springer Texts in Statistics |language=en |doi=10.1007/0-387-30623-4|isbn=978-0-387-25145-5 }}</ref> ...

{{Short description|Bayesian nonparametric model of probability distributions}} ...istics, the [[Dirichlet process]] (DP) is one of the most popular Bayesian nonparametric models. It was ...