- Referenced in 936 articles
- smoothers the authors concentrate on the simplest nonparametric curve estimation setting, namely density and regression ... book only a basic knowledge of statistics, calculus and matrix algebra is assumed...
- Referenced in 419 articles
- statistical tools for a wide range of statistical analyses, including analysis of variance, regression, categorical ... survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis. A few examples include mixed models...
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- Plus language for data analysis, statistics, and scientific computing. The module is oriented towards engineers ... diverse as data visualization and analysis, nonparametric statistical estimation, signal and image compression, signal processing...
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- accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition. This package was designed ... editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible...
- Referenced in 706 articles
- this book the authors argue that nonparametric regression can be viewed as a relatively simple ... that they are aiming at consists of statistically oriented scientists who have a good working...
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- probability theory, mathematical statistics, multivariate statistics, nonparametric statistics, sampling survey, linear models, time series, computational...
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- functions to perform the methods in nonparametric regression and density estimation, described in Davies ... Strings and Multiresolution (with discussion) Annals of Statistics. 29. p1-65 Davies...
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- nonparametric regression models. Linear models are central to the practice of statistics and form ... foundation of a vast range of statistical methodologies. Julian J. Faraway’s critically acclaimed Linear ... linear models (GLMs), mixed effect models, and nonparametric regression models. The author’s treatment ... even the use of neural networks in statistics. To demonstrate the interplay of theory...
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- MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. MatchIt implements the suggestions of Ho, Imai, King ... improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide ... hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into...
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- Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit...
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- package npsp: Nonparametric Spatial Statistics. Multidimensional nonparametric spatio-temporal (geo)statistics. S3 classes and methods ... polynomial kernel regression, density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial...
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- from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive ... particular, BART is defined by a statistical model: a prior and a likelihood. This approach...
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- cmine implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family...
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- between two copulas. This package implements the nonparametric test of equality between two copulas proposed ... values of the Cramér-von Mises test statistic. Finite sample properties are assessed with Monte...
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- dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between...
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- package WRS2. A collection of robust statistical methods based on Wilcox’ WRS functions. It implements ... quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures...
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- CMARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous ... optimization Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate...
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- SnPM - Statistical NonParametric Mapping - A toolbox for SPM. The Statistical nonParametric Mapping toolbox provides ... General Linear Model and pseudo t-statistics for independent observations...
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- goodness-of-fit statistics, hypothesis testing, checks for identifiability, parametric and nonparametric bootstrapping, and power...
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- CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach...