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Theorem in statistics and econometrics
Frisch–Waugh–Lovell theorem, Frisch-Waugh theorem, partitioned regression theorem, residual regression, and the regression anatomy theorem. While Frisch and
Frisch–Waugh–Lovell_theorem
Economic price theory
The Regression Theorem, first proposed by Ludwig von Mises in his 1912 book The Theory of Money and Credit, states that the value of money can be traced
Regression_theorem
Quantum regression theorem (QRT) is a result in quantum statistical mechanics and quantum optics that provides a rule for computing multi-time correlation
Quantum_regression_theorem
Theorem related to ordinary least squares
estimator (which also drops linearity), ridge regression, or simply any degenerate estimator. The theorem was named after Carl Friedrich Gauss and Andrey
Gauss–Markov_theorem
Method for estimating the unknown parameters in a linear regression model
especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent
Ordinary_least_squares
1912 book by Ludwig von Mises
cycle theory. The book also includes the first exposition of Mises's regression theorem, which aimed to explain the purchasing power of money using the subjective
The Theory of Money and Credit
The_Theory_of_Money_and_Credit
Statistical modeling method
regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression
Linear_regression
Set of statistical processes for estimating the relationships among variables
called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which
Regression_analysis
Statistics concept
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable
Polynomial_regression
Statistical modeling technique
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional
Quantile_regression
Specialized form of regression analysis, in statistics
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship
Robust_regression
School of economic thought
Bitcoin is at odds with certain Austrian principles, such as Mises' regression theorem, which holds that money must originate from a commodity with prior
Austrian_school_of_economics
Regularization technique for ill-posed problems
Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models
Ridge_regression
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
Fundamental theorem in probability theory and statistics
In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample
Central_limit_theorem
Statistical model for count data
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes
Poisson_regression
Statistical phenomenon
In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where
Regression_toward_the_mean
Statistical model for a binary dependent variable
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model
Logistic_regression
Concept in statistical mathematics
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable
Segmented_regression
Moving average and polynomial regression method for smoothing data
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its
Local_regression
Regression analysis for modeling ordinal data
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e.
Ordinal_regression
Concept in regression analysis mathematics
using the epsilon-insensitive loss leads to support vector regression. The representer theorem guarantees that the solution can be written as: f ( x ) =
Regularized_least_squares
Statistics concept
distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead
Errors_and_residuals
Statistical method
squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of
Partial least squares regression
Partial_least_squares_regression
Approximation method in statistics
as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty is
Least_squares
Type of numerical analysis
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations
Isotonic_regression
Statistical tool used in meta-analyses
Meta-regression is a meta-analysis that uses regression analysis to combine, compare, and synthesize research findings from multiple studies while adjusting
Meta-regression
Regression analysis technique
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is
Binomial_regression
Method for model fitting in statistics
(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance
Weighted_least_squares
Regression for more than two discrete outcomes
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than
Multinomial logistic regression
Multinomial_logistic_regression
Statistical hypothesis test
the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4. From the regression, the slope
Student's_t-test
Limitative results in mathematical logic
Gödel's incompleteness theorems are two theorems of mathematical logic that are concerned with the limits of provability in formal axiomatic theories
Gödel's incompleteness theorems
Gödel's_incompleteness_theorems
Statistical method
testing. In regression problems, case resampling refers to the simple scheme of resampling individual cases – often rows of a data set. For regression problems
Bootstrapping_(statistics)
Regression analysis
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination
Nonlinear_regression
Mathematical rule for inverting probabilities
Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes (/beɪz/), gives a mathematical rule for inverting conditional probabilities
Bayes'_theorem
Algorithm for the line of best fit for a two-dimensional dataset
data-sources; however the regression procedure takes no account for possible errors in estimating this ratio. The Deming regression is only slightly more
Deming_regression
Statistical property
measure of the dispersion of sample means around the population mean. In regression analysis, the term "standard error" refers either to the square root of
Standard_error
How many standard deviations apart from the mean an observed datum is
to multiple regression analysis is sometimes used as an aid to interpretation. (page 95) state the following. "The standardized regression slope is the
Standard_score
Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation
List_of_statistics_articles
Theory and paradigm of statistics
Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Bayes' theorem describes the conditional probability
Bayesian_statistics
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Class of statistical models
(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the
Generalized_linear_model
Statistical regression technique
multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression): The multilevel regression model
Multilevel regression with poststratification
Multilevel_regression_with_poststratification
Least squares approximation of linear functions to data
^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least
Linear_least_squares
Statistical technique
taken into account. It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models
Total_least_squares
Statistics concept
regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,
Regression_validation
Regression model for ordinal dependent variables
logit model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first
Ordered_logit
Statistical technique
used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the
Principal component regression
Principal_component_regression
Category of regression analysis
Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information
Nonparametric_regression
Statistical linear model
model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is
General_linear_model
Type of statistical model
can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became
Multilevel_model
Collection of statistical models
notation in place, we now have the exact connection with linear regression. We simply regress response y k {\displaystyle y_{k}} against the vector X k {\displaystyle
Analysis_of_variance
Statistical method
parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y
Regression discontinuity design
Regression_discontinuity_design
Statistical theorem
In statistics, Wilks' theorem offers an asymptotic distribution of the log-likelihood ratio statistic, which can be used to produce confidence intervals
Wilks'_theorem
Statistical hypothesis test
that a proposed regression model fits the data well. See Lack-of-fit sum of squares. The hypothesis that a data set in a regression analysis follows
F-test
Statistical principle
on an assumption of the distributional form (see Pitman–Koopman–Darmois theorem below), but remained very important in theoretical work. Roughly, given
Sufficient_statistic
Statistical model
including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a
Fixed_effects_model
Approximation method in statistics
the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x ,
Non-linear_least_squares
Statement of infinite regress
such as the regress argument in epistemology. Early variants of the saying do not always have explicit references to infinite regression (i.e., the phrase
Turtles_all_the_way_down
Statistical estimation method
In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output
Binary_regression
Number of values in the final calculation of a statistic that are free to vary
regression methods, including regularized least squares (e.g., ridge regression), linear smoothers, smoothing splines, and semiparametric regression,
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Statistical estimation technique
parameters in a linear regression model. It is used when there is a non-zero amount of correlation between the residuals in the regression model. GLS is employed
Generalized_least_squares
Type of statistics
their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between
Robust_statistics
Correlation of a signal with a time-shifted copy of itself, as a function of shift
whether or not the regressors include lags of the dependent variable, is the Breusch–Godfrey test. This involves an auxiliary regression, wherein the residuals
Autocorrelation
Statistical property
classical linear regression model is that there is no heteroscedasticity. Breaking this assumption means that the Gauss–Markov theorem does not apply,
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Branch of statistics
time-varying covariates. The Cox PH regression model is a linear model. It is similar to linear regression and logistic regression. Specifically, these methods
Survival_analysis
Measure of linear correlation
Standardized covariance Standardized slope of the regression line Geometric mean of the two regression slopes Square root of the ratio of two variances
Pearson correlation coefficient
Pearson_correlation_coefficient
Regression models accounting for possible errors in independent variables
error model is a regression model that accounts for measurement errors in the independent variables. In contrast, standard regression models assume that
Errors-in-variables_model
Simultaneous observation and analysis of more than one outcome variable
problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate
Multivariate_statistics
General linear model that blends ANOVA and regression
linear regression assumptions hold; further we assume that the slope of the covariate is equal across all treatment groups (homogeneity of regression slopes)
Analysis_of_covariance
Class of statistical survival models
itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which is sometimes
Proportional_hazards_model
Branch of statistics
the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function
Mathematical_statistics
Experimental design that is optimal with respect to some statistical criterion
criterion results in minimizing the average variance of the estimates of the regression coefficients. C-optimality This criterion minimizes the variance of a
Optimal_experimental_design
Statistical theorem in the analysis of variance
In statistics, Cochran's theorem, devised by William G. Cochran, is a theorem used to justify results relating to the probability distributions of statistics
Cochran's_theorem
Categorization of data using statistics
logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.)
Statistical_classification
Concept in probability theory and statistics
for including other right-side variables in a multiple regression; but while multiple regression gives unbiased results for the effect size, it does not
Partial_correlation
Metric for fit of statistical models
Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness
Goodness_of_fit
Normality test
David Lilien, et al. (1995) when using this test along with multiple regression analysis the right estimate is: J B = n − k 6 ( S 2 + 1 4 ( K − 3 ) 2
Jarque–Bera_test
Statistical optimality criterion
the idea of least absolute deviations regression is just as straightforward as that of least squares regression, the least absolute deviations line is
Least_absolute_deviations
Process of using data analysis for predicting population data from sample data
assumptions of Normality in the population also invalidates some forms of regression-based inference. The use of any parametric model is viewed skeptically
Statistical_inference
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
Statistical regression where the dependent variable can take only two values
In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word
Probit_model
Application of a function to each point in a data set
with linear regression if the original data violates one or more assumptions of linear regression. For example, the simplest linear regression models assume
Data transformation (statistics)
Data_transformation_(statistics)
Method for solving certain optimization problems
maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers
Iteratively reweighted least squares
Iteratively_reweighted_least_squares
Variable capable of taking on a limited number of possible values
distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"
Categorical_variable
Statistical model allowing for frequent zero values
distribution or a negative binomial distribution. Hilbe notes that "Poisson regression is traditionally conceived of as the basic count model upon which a variety
Zero-inflated_model
Test statistic
when using OLS regression gretl: Automatically calculated when using OLS regression Stata: the command estat dwatson, following regress in time series
Durbin–Watson_statistic
Function related to statistics and probability theory
{\text{HH}})=0.25} , a conclusion which could only be reached via Bayes' theorem given knowledge about the marginal probabilities P ( p H = 0.5 ) {\textstyle
Likelihood_function
Statistical theorem
In statistics, the Rao–Blackwell theorem, sometimes referred to as the Rao–Blackwell–Kolmogorov theorem, is a result that characterizes the transformation
Rao–Blackwell_theorem
Derivation of the laws of probability theory
Cox's theorem, named after the physicist Richard Threlkeld Cox, is a derivation of the laws of probability theory from a certain set of postulates. This
Cox's_theorem
Tools to represent statistical uncertainty
probability function. Confidence bands commonly arise in regression analysis. In the case of a simple regression involving a single independent variable, results
Confidence and prediction bands
Confidence_and_prediction_bands
Transforming data by taking the logarithm
with linear regression if the original data violates one or more assumptions of linear regression. For example, the simplest linear regression models assume
Log transformation (statistics)
Log_transformation_(statistics)
Statistical model containing both fixed effects and random effects
Mixed models are often preferred over traditional analysis of variance regression models because they don't rely on the independent observations assumption
Mixed_model
Branch of statistics
(due to the central limit theorem and the delta method). Least square estimation (LSE): This method applies to a regression setting, where the data arises
Parametric_statistics
Diagnostic plot of binary classifier ability
Notable proposals for regression problems are the so-called regression error characteristic (REC) Curves and the Regression ROC (RROC) curves. In the
Receiver operating characteristic
Receiver_operating_characteristic
Overview of and topical guide to machine learning
(SOM) Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)
Outline_of_machine_learning
Empirical law on the variance of species in a habitat
error of the regression, α and β are the constant and slope of the regression respectively, sβ2 is the variance of the slope of the regression, N is the
Taylor's_law
Statistical matching technique
control group—based on observed predictors, usually obtained from logistic regression to create a counterfactual group. Propensity scores may be used for matching
Propensity_score_matching
Statistical test
however, not actually t-distributed except for the special case of linear regression with normally distributed errors. In general, it follows an asymptotic
Wald_test
Sequence of data points over time
simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial
Time_series
REGRESSION THEOREM
REGRESSION THEOREM
Surname or Lastname
English (mainly southwest England)
English (mainly southwest England) : topographic name for someone who lived by a depression or low-lying spot, from Old English holh ‘hole’, ‘hollow’, ‘depression’.Norwegian : habitational name from any of numerous farmsteads, so named from the dative singular or indefinite plural form of Old Norse hóll ‘round hill’, ‘mound’.Shortened form of Dutch van (den) Hole, a habitational name from the common place name Hol, meaning ‘hollow’, ‘depression’, ‘valley’, or a topographic name from the same term.
Surname or Lastname
English (chiefly West Midlands)
English (chiefly West Midlands) : nickname for a trustworthy person, from Middle English trow(e), trew(e) ‘faithful’, ‘steadfast’.English : variant of Tree, from Middle English trow, trew.English : topographic name for someone who lived near a depression in the ground, from Middle English trow ‘trough’, ‘hollow’.Translated form of French Jetté (see Jette). Trow represents the French Canadian pronunciation of English ‘throw’.
Surname or Lastname
English (Yorkshire and Lancashire)
English (Yorkshire and Lancashire) : topographic name for someone who lived by a depression or low-lying spot, from Old English holh ‘hole’, ‘hollow’, ‘depression’ (see Hole).Irish : reduced Anglicized form of Gaelic Mac Giolla Chomhghaill, a patronymic from a personal name meaning ‘devotee of (Saint) Comhghal’ (see McCool). Woulfe, however, traces Hoyle (as well as MacIlhoyle and McElhill) to Mac Giolla Choille ‘son of the lad of the wood’, which has sometimes been translated as Woods.
Male
Greek
(Καϊάφας) Greek form of Aramaic Qayyafa ("depression"), KAIAPHAS means "as comely." In the New Testament bible, this is the name of a high priest of the Jews.Â
Boy/Male
Arabic, Muslim
Leadership; Individuality; Aggression; Self-confidence; Originality; Impatience.
Surname or Lastname
English
English : from a medieval personal name, a short form of Philpott.English : topographic name for someone who lived by a depression in the ground, from Middle English pot ‘drinking or storage vessel’ used in this transferred sense, or a habitational name from one of the minor places deriving their name from this word, in the sense ‘pit’, ‘hole’.English and North German (Lower Rhine-Westphalia) : metonymic occupational name for a potter, from Middle English, Middle Low German pot ‘pot’. See also Potter.North German : topographic name for someone living on a low-lying plot, from Low German dialect pÅt ‘puddle’.
REGRESSION THEOREM
REGRESSION THEOREM
Biblical
same as Naphtali
Boy/Male
Australian, Christian, Jamaican
Precious Stone
Male
Hindi/Indian
(हरà¥à¤¶à¤¦) Variant form of Hindi Harsha, HARSHAD means "happiness."
Boy/Male
Tamil
The Sun (Son of Aditi)
Girl/Female
Tamil
Anjasi | அநà¯à®œà®¾à®¸à¯€
Honest, Morally upstanding
Male
Portuguese
Portuguese form of French Tristan, probably TRISTÃO means "riot, tumult."
Boy/Male
Muslim/Islamic
Fearless
Girl/Female
French, German, Teutonic
Renowned for War
Boy/Male
Gujarati, Hindu, Indian
Welfare; King
Girl/Female
Indian
REGRESSION THEOREM
REGRESSION THEOREM
REGRESSION THEOREM
REGRESSION THEOREM
REGRESSION THEOREM
n.
Depression of spirits; discouragement.
n.
Dejection; depression.
n.
The act of ceding back; restoration; repeated cession; as, the recession of conquered territory to its former sovereign.
n.
The act of going; egress.
n.
The first attack, or act of hostility; the first act of injury, or first act leading to a war or a controversy; unprovoked attack; assault; as, a war of aggression. "Aggressions of power."
adv.
By way of digression.
adv.
In harmonical progression.
n.
Course; passage; lapse or process of time.
adv.
In a regressive manner.
n.
Depression of the jaw; hence, depression of spirits.
n.
The act of repressing, or state of being repressed; as, the repression of evil and evil doers.
n.
That which represses; check; restraint.
n.
The act of passing back or returning; retrogression; retrogradation.
n.
Aggression.
n.
A regular succession of tones or chords; the movement of the parts in harmony; the order of the modulations in a piece from key to key.
n.
A cavity; a depression.
n.
Regular or proportional advance in increase or decrease of numbers; continued proportion, arithmetical, geometrical, or harmonic.
n.
Digression.
n.
A casting down; depression.
n.
The act of moving forward; a proceeding in a course; motion onward.