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Branch of statistics
Parametric statistics is a branch of statistics that is concerned with the analysis of and inference from data assuming that the underlying distribution
Parametric_statistics
Type of statistical analysis
than finite dimensional, as in parametric statistics. Nonparametric statistics can be used for descriptive statistics or statistical inference. Nonparametric
Nonparametric_statistics
Variable used for specification
particular parametric family of probability distributions. In that case, one speaks of non-parametric statistics as opposed to the parametric statistics just
Parameter
Topics referred to by the same term
of a variable Parametric statistics, a branch of statistics that assumes data has come from a type of probability distribution Parametric derivative, a
Parametric
Process of using data analysis for predicting population data from sample data
class of parametric models. Non-parametric: The assumptions made about the process generating the data are much less than in parametric statistics and may
Statistical_inference
Type of statistical model
In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric
Parametric_model
Branch of statistics
inferential statistics. The typical parameters are the expectations, variance, etc. Unlike parametric statistics, nonparametric statistics make no assumptions
Mathematical_statistics
Measure of linear correlation
confidence intervals for Pearson's correlation coefficient. In the "non-parametric" bootstrap, n pairs (xi, yi) are resampled "with replacement" from the
Pearson correlation coefficient
Pearson_correlation_coefficient
Type of statistics
significantly higher standard deviation (representing outliers). Robust parametric statistics can proceed in two ways: by designing estimators so that a pre-selected
Robust_statistics
Statistical hypothesis test
may have better type-1 error control than some non-parametric alternatives. Furthermore, non-parametric methods, such as the Mann-Whitney U test discussed
Student's_t-test
Statistical modeling method
used to non-parametrically estimate the distribution of the error term. Bayesian linear regression applies the framework of Bayesian statistics to linear
Linear_regression
Collection of statistical models
unit-treatment additivity. If the response variable is expected to follow a parametric family of probability distributions, then the statistician may specify
Analysis_of_variance
Problem in statistical estimation
a Population" (PDF). Technical Report SFB 386, No. 399, Department of Statistics, Ludwig-Maximilians-Universität München. Retrieved 17 April 2016. Johnson
German_tank_problem
Measure of similarity between samples
In statistics, biweight midcorrelation (also called bicor) is a measure of similarity between samples. It is median-based, rather than mean-based, thus
Biweight_midcorrelation
Ratio in statistics
In statistics, the t-statistic is the ratio of the difference in a number’s estimated value from its assumed value to its standard error. It is used in
T-statistic
Relationship between items in a set
see. Analysis of data obtained by ranking commonly requires non-parametric statistics. It is not always possible to assign rankings uniquely. For example
Ranking
Statistical method for fitting a line
In non-parametric statistics, the Theil–Sen estimator is a method for robustly fitting a line to sample points in the plane (a form of simple linear regression)
Theil–Sen_estimator
Class of statistical tests
In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random
Normality_test
Kth smallest value in a statistical sample
k\leq n} . Together with rank statistics, order statistics are among the most fundamental tools in non-parametric statistics and inference. Important special
Order_statistic
Method for estimating the unknown parameters in a linear regression model
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model by
Ordinary_least_squares
Empirical statistical testing of economic theories
counterfactual post hoc, thereby justifying the use of parametric statistical tools. Since parametric statistics depends on any observation following a Gaussian
Econometrics
Regression algorithm
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron
Least-angle_regression
nonparametric and parametric statistics SuperCROSS – comprehensive statistics package with ad-hoc, cross tabulation analysis Systat – general statistics package
List_of_statistical_software
French statistician
Paris and at the University of Paris-Est, known for her work on non-parametric statistics, density estimation, and deconvolution. Butucea completed her Ph
Cristina_Butucea
Method of estimating the median of a population
Pareto interpolation is a method of estimating the median and other properties of a population that follows a Pareto distribution. It is used in economics
Pareto_interpolation
Goodness-of-fit measure in statistics
In statistics, D'Agostino's K2 test, named for Ralph D'Agostino, is a goodness-of-fit measure of departure from normality, that is the test aims to gauge
D'Agostino's_K-squared_test
Type of interaction between species
it renders parametric statistics as commonly used by biologists invalid. Log-transformation of data before the application of parametric test, or the
Parasitism
Statistical noise
In statistics, the fraction of variance unexplained (FVU) in the context of a regression task is the fraction of variance of the regressand (dependent
Fraction of variance unexplained
Fraction_of_variance_unexplained
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
distribution makes sampling difficult and invalidates commonly used parametric statistics. A similar pattern is found among predators that search for their
Aggregated_distribution
Multi-attribute global inference of quality (MAGIQ) is a multi-criteria decision analysis technique. MAGIQ is based on a hierarchical decomposition of
Multi-attribute global inference of quality
Multi-attribute_global_inference_of_quality
Number and virulence of the parasites that a host organism harbours
use of parametric statistics should be avoided. Log-transformation of data before the application of parametric test, or the use of non-parametric statistics
Parasite_load
Family of probability distributions
In probability theory, especially in mathematical statistics, a location–scale family is a family of probability distributions parametrized by a location
Location–scale_family
Study of collection and analysis of data
Statistics (from German: Statistik, orig. "description of a state, a country") is the discipline that concerns the collection, organization, analysis,
Statistics
Statistical test
erroneously low p-values. See, e.g., Guillot and Rousset (2013). Non-parametric statistics Sørensen–Dice coefficient Mantel, N. (1967). "The detection of disease
Mantel_test
Concept in statistics
that it encompasses and unifies a wide range of examples, from regular parametric cases (including most examples of the classical development of Fisher's
Confidence_distribution
Format for presentation of quantitative data
order, thereby easing the move to order-based inference and non-parametric statistics. To construct a stem-and-leaf display, the observations must first
Stem-and-leaf_display
In probability theory, especially as it is used in statistics, a group family of probability distributions is one obtained by subjecting a random variable
Group_family
Statistical data type
developed for the analysis of ranked measurements. However, the use of parametric statistics for ordinal data may be permissible with certain caveats to take
Ordinal_data
Tree-based ensemble machine learning methods
Ensemble learning – Statistics and machine learning technique Gradient boosting – Machine learning technique Non-parametric statistics – Type of statistical
Random_forest
Overview of and topical guide to statistics
estimator Consistent estimator Efficiency (statistics) Completeness (statistics) Non-parametric statistics Nonparametric regression Kernels Kernel method
Outline_of_statistics
Category of regression analysis
completely constructed using information derived from the data. That is, no parametric equation is assumed for the relationship between predictors and dependent
Nonparametric_regression
Statistical measure
In probability theory and statistics, a scale parameter is a special kind of numerical parameter of a parametric family of probability distributions. The
Scale_parameter
Number taken as representative of a list of numbers
means (or "measures of central tendency") in mathematics, especially in statistics. Each attempts to summarize or typify a given group of data, illustrating
Average
Exact statistical hypothesis test
usual partitioning approach. Permutation tests are a subset of non-parametric statistics. Assuming that our experimental data come from data measured from
Permutation_test
Stochastic process in probability theory
Applications of the theory of empirical processes arise in non-parametric statistics. For X1, X2, ... Xn independent and identically-distributed random
Empirical_process
Regression models that combine parametric and nonparametric models
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations
Semiparametric_regression
Concept in statistics
common in machine learning. In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in
Kernel_(statistics)
Parameter identification problem Parameter space Parametric family Parametric model Parametric statistics Pareto analysis Pareto chart Pareto distribution
List_of_statistics_articles
Statistical procedure
only distances are meaningful, but not ratios). In theoretical statistics, parametric normalization can often lead to pivotal quantities – functions whose
Normalization_(statistics)
Labor Statistics, developed software for nuclear simulations Cristina Butucea, French statistician known for her work on non-parametric statistics, density
List_of_women_in_statistics
Graphical technique for data sets
order, thereby easing the move to order-based inference and non-parametric statistics. Star plot : A graphical method of displaying multivariate data
Plot_(graphics)
Statistical hypothesis test
The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on
Wilcoxon_signed-rank_test
Statistical method
Review of Economics and Statistics. 90 (3): 414–427. doi:10.1162/rest.90.3.414. Hanley JA, MacGibbon B (2006). "Creating non-parametric bootstrap samples using
Bootstrapping_(statistics)
Statistical distribution for dependence between random variables
are many parametric copula families available, which usually have parameters that control the strength of dependence. Some popular parametric copula models
Copula_(statistics)
Statistical measure of association
2016-08-16 at the Wayback Machine) Sheskin, David J. (1997). Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, Fl: CRC Press. Liebetrau
Cramér's_V
Theory and paradigm of statistics
_{\Omega },\lbrace P_{\theta }\mid \theta \in \Theta \rbrace )} to be some parametric statistical model and ( Θ , Σ Θ , π ) {\displaystyle (\Theta ,\Sigma _{\Theta
Bayesian_statistics
German chemist and statistician (1902–1962)
the late 1920s onwards he also contributed to the theory of non-parametric statistics, and developed methods for cases involving single and two samples
Walter-Ulrich_Behrens
Technique in information theory
generalized the classical notion of minimal sufficient statistics from parametric statistics to arbitrary distributions, not necessarily of exponential
Information_bottleneck_method
mark the 9th and 91st percentiles. Arthur Bowley used a set of non-parametric statistics, called a "seven-figure summary", including the extremes, deciles
Seven-number_summary
Siegel–Tukey test, named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to data measured at least on an ordinal scale
Siegel–Tukey_test
Type of statistics
while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistics is distinguished from
Descriptive_statistics
American psychologist (1916–1961)
who became especially well known for his work in popularizing non-parametric statistics for use in the behavioral sciences. He was a co-developer of the
Sidney_Siegel
dichotomous. Assumptions, parametric and non-parametric: There are two groups of statistical tests, parametric and non-parametric. The choice between these
List_of_statistical_tests
American statistician (born 1910)
contributions were in the fields of statistical decision theory, non-parametric statistics, sequential analysis, and information theory. One of his results
Jacob_Wolfowitz
distribution of group members among groups, the application of parametric statistics would be misleading. Another problem arises when analyzing crowding
Group_size_measures
Parametric model in survival analysis
survival analysis, an accelerated failure time model (AFT model) is a parametric model that provides an alternative to the commonly used proportional hazards
Accelerated failure time model
Accelerated_failure_time_model
Probability of survival beyond any specified time
textbooks on survival analysis. Lawless has extensive coverage of parametric models. Parametric survival functions are commonly used in manufacturing applications
Survival_function
Type of statistical model
In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components. A statistical model is a parameterized
Semiparametric_model
Smooth function in statistics
non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance functions take on a parametric form
Variance_function
the data, it is one of the non-parametric methods. It is an extension of the Kruskal–Wallis test, the non-parametric equivalent for one-way analysis
Scheirer–Ray–Hare_test
level exactly.[citation needed] Parametric tests, such as those used in exact statistics, are exact tests when the parametric assumptions are fully met, but
Exact_test
Concept in statistics
In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method
Kernel_density_estimation
Species of moth
browntail moth local outbreaks by combining life table data and semi-parametric statistics". Ecological Entomology. 36 (2): 188–199. Bibcode:2011EcoEn..36
Brown-tail_moth
American statistician (1915–1991)
Statistical Association and the Institute of Mathematical Statistics. As an expert on non-parametric statistics, he wrote over 50 articles and six books. He also
Gottfried_E._Noether
Moving average and polynomial regression method for smoothing data
both pronounced /ˈloʊɛs/ LOH-ess. They are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based
Local_regression
Type of probability distribution
erroneous distribution. Parametric statistics that assume no error often fail on such mixture densities – for example, statistics that assume normality
Mixture_distribution
Family of statistical methods based on sampling of available data
alternative to inference based on parametric assumptions when those assumptions are in doubt, or where parametric inference is impossible or requires
Resampling_(statistics)
Canadian astronomer
Abraham's career has been notable for his contributions via non-parametric statistics to galaxy morphological classification, especially at high-redshift
Roberto_Abraham
Optimization technique
Determine a good natural scale for the texture elements. Compute non-parametric statistics of the model-interior texons, either on intensity or on Gabor filter
Graph cuts in computer vision and artificial intelligence
Graph_cuts_in_computer_vision_and_artificial_intelligence
Free and open-source statistical program
regression, and binomial tests. Survival Analyses: non-parametric, semi-parametric, parametric Time Series: Time series analysis with Descriptives, Stationarity
JASP
Probability theory for low quality data
provides a formal framework for models used in robust statistics and non-parametric statistics. Included are also concepts based on Choquet integration
Imprecise_probability
Selection of data points in statistics
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to
Sampling_(statistics)
Class of statistics in estimation theory
families have been recognized as U-statistics for general distributions. In non-parametric statistics, the theory of U-statistics is used to establish for statistical
U-statistic
American social psychologist (1919–1989)
though less frequently cited, contribution to the field of non-parametric statistics. In 1946, he published an independent formulation of a rank-sum
Leon_Festinger
Sampling from a population which can be partitioned into subpopulations
In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when
Stratified_sampling
Lab technique in cellular biology
with many personnel). Methods: Most tools use regression or non-parametric statistics to identify differentially expressed genes, and are either based
RNA-Seq
These terms and concepts are used in the mathematical sciences of statistics and probability, their sub-disciplines, and related fields. For additional
Glossary of probability and statistics
Glossary_of_probability_and_statistics
Designs for Parametric Models". In: Ghosh, S. and Rao, C. R., (Eds) (1996). "Design and Analysis of Experiments," Handbook of Statistics, Volume 13. North-Holland
History_of_statistics
Family of stochastic processes
information. This is a common phenomenon in the context of Bayesian non-parametric statistics where a typical task is to learn distributions on function spaces
Dirichlet_process
Non-parametric statistical test
The Friedman test is a non-parametric statistical test developed by Milton Friedman. Similar to the parametric repeated measures ANOVA, it is used to
Friedman_test
Middle quantile of a data set or probability distribution
Retrieved 25 February 2013. David J. Sheskin (27 August 2003). Handbook of Parametric and Nonparametric Statistical Procedures (Third ed.). CRC Press. p. 7
Median
Mathematician (1912–1999)
mathematician, known for his contributions to number theory, non-parametric statistics, empirical distribution, Cramér–Rao inequality, and information
István_Vincze_(mathematician)
In statistics, the Conover squared ranks test is a non-parametric version of the parametric Levene's test for equality of variance. Conover's squared ranks
Squared_ranks_test
Statistical measure of how far values spread from their average
for the equality of two or more variances more difficult. Several non parametric tests have been proposed: these include the Barton–David–Ansari–Freund–Siegel–Tukey
Variance
Chinese/British statistician (born 1944)
contributions to semi-parametric statistics, non-parametric statistics, dimension reduction, model selection, likelihood-free statistics, gradient-based entropy
Howell_Tong
Medical research
was present) and inappropriate statistics (for example, multiple t-tests without correction or use of parametric statistics on non-normally distributed data)
Clinical trials on glucosamine and chondroitin
Clinical_trials_on_glucosamine_and_chondroitin
Simultaneous observation and analysis of more than one outcome variable
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e.
Multivariate_statistics
Family of probability distributions related to the normal distribution
In probability and statistics, an exponential family is a parametric set of probability distributions of a certain form, specified below. This special
Exponential_family
In mathematics and its applications, a parametric family or a parameterized family is a family of objects (a set of related objects) whose differences
Parametric_family
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS
PARAMETRIC STATISTICS