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...sampling |url=https://compcogsci-3016.djnavarro.net/lecture20.pdf |website=Computational Cognitive Science}}</ref> while in weak sampling, it is assumed that the da [[Category:Sampling (statistics)]] ...

...he '''signal magnitude area''' (abbreviated '''SMA''' or '''sma''') is a [[statistics|statistical]] measure of the [[magnitude (mathematics)|magnitude]] of a var ...of values (or a [[continuous-time]] [[waveform]]) is the [[Normalization (statistics)|normalized]] [[integral]] of the original values. ...

...uares''' ('''LTS'''), or '''least trimmed sum of squares''', is a [[robust statistics|robust statistical method]] that fits a function to a set of data whilst no where the [[errors and residuals in statistics|residuals]] are defined as the differences between the values of the [[Depe ...

| discipline = Statistics '''Mary Catherine Silber''' is a professor in the Department of Statistics at the [[University of Chicago]] who works on dynamical systems, in [[bifur ...

{{short description|Computational geometry concept}} In [[statistics]] and [[computational geometry]], the '''Tukey depth''' <ref>{{cite book |last1=Tukey |first1=Joh ...

...es for computing bivariate normal probabilities". ''Annals of Mathematical Statistics'', ...1984). "Multivariate normal probabilities with [[error bound]]". ''Applied Statistics'', 33, 81&ndash;94.</ref> ...

In [[computational geometry]], the '''convex layers''' of a set of points in the [[Euclidean p | journal = Journal of Computational Geometry ...

....|last2=Ridder|first2=A.|title=Variance Reduction|journal= Wiley StatsRef: Statistics Reference Online|date=2017|pages=1–6|doi=10.1002/9781118445112.stat07975|is [[Category:Computational statistics]] ...

{{Short description|Probability distribution in applied statistics}} In applied statistics, the '''Marshall–Olkin exponential distribution''' is any member of a certa ...

[[Category:Computational statistics]] {{Statistics-stub}} ...

In [[applied mathematics]] and [[statistics]], '''basis pursuit denoising''' (BPDN) refers to a [[mathematical optimiza ...e, the unconstrained formulation, for which most specialized and efficient computational algorithms are developed, is usually preferred. The unconstrained formulat ...

...ref>Scott DW. Scott's rule. Wiley Interdisciplinary Reviews: Computational Statistics. 2010 Jul; 2(4):497–502.</ref> ...D.W. | year = 2009 | title = Sturges' rule | journal = WIREs Computational Statistics | volume = 1 | issue = 3| pages = 303–306 | doi=10.1002/wics.35| s2cid = 19 ...

...in the [[likelihood function]]. This, however, substantially reduces the [[computational complexity]] of the optimization problem. ==Statistics used in estimation== ...

In [[machine learning]] and [[computational learning theory]], '''LogitBoost''' is a [[Boosting (meta-algorithm)|boosti ...ive logistic regression: a statistical view of boosting |journal=Annals of Statistics |volume=28 |issue=2 |year=2000 |pages=337–407 |citeseerx=10.1.1.51.9525 |do ...

{{Short description|Type of probability distribution used in statistics}} ...irst=A. |editor2-last=Zellner |series=Studies in Bayesian Econometrics and Statistics |volume=6 |location=New York |publisher=Elsevier |pages=233–243 |isbn=978-0 ...

In [[statistics]], the '''precision matrix''' or '''concentration matrix''' is the [[matrix ...first1=W.M. | last2=Curran | first2=J.M. | title=Introduction to Bayesian Statistics | publisher=Wiley | year=2016 | isbn=978-1-118-59315-8 | url=https://books. ...

'''Bootstrapping populations''' in [[statistics]] and [[mathematics]] starts with a [[Statistical sample|sample]] <math>\{x ...rs, we denote with a vector <math>\boldsymbol\theta</math>, a [[Parametric statistics|parametric inference]] problem consists of computing suitable values – call ...

{{Expert needed|statistics|date=June 2009|talk=Expert tag}} ...ned area. The term complete spatial randomness is commonly used in Applied Statistics in the context of examining certain point patterns, whereas in most other s ...

.../1177700372 |url=https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-35/issue-4/Sufficiency-and-Approximate-Sufficiency/10.1214/aoms/1177 ...ymptotic methods in statistical decision theory |series=Springer Series in Statistics |publisher=Springer, New York|date=1986 |pages=1–5 |doi=10.1007/978-1-4612- ...

In [[robust statistics]] and [[computational geometry]], '''simplicial depth''' is a measure of [[central tendency]] det It possible to construct a data structure using [[ε-net (computational geometry)|ε-nets]] that can approximate the simplicial depth of a query poi ...