# Gauss–Markov theorem Gauss–Markov theorem Not to be confused with Gauss–Markov process. "BLUE" redireciona aqui. For queue management algorithm, see Blue (queue management algorithm). Part of a series on Regression analysis Models Linear regressionSimple regressionPolynomial regressionGeneral linear model Generalized linear modelDiscrete choiceBinomial regressionBinary regressionLogistic regressionMultinomial logistic regressionMixed logitProbitMultinomial probitOrdered logitOrdered probitPoisson Multilevel modelFixed effectsRandom effectsLinear mixed-effects modelNonlinear mixed-effects model Nonlinear regressionNonparametricSemiparametricRobustQuantileIsotonicPrincipal componentsLeast angleLocalSegmented Errors-in-variables Estimation Least squaresLinearNon-linear OrdinaryWeightedGeneralized PartialTotalNon-negativeRidge regressionRegularized Least absolute deviationsIteratively reweightedBayesianBayesian multivariateLeast-squares spectral analysis Background Regression validationMean and predicted responseErrors and residualsGoodness of fitStudentized residualGauss–Markov theorem Mathematics portal vte In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) states that the ordinary least squares (OLS) estimator has the lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances and expectation value of zero. The errors do not need to be normal, nor do they need to be independent and identically distributed (only uncorrelated with mean zero and homoscedastic with finite variance). The requirement that the estimator be unbiased cannot be dropped, since biased estimators exist with lower variance. Ver, por exemplo, the James–Stein estimator (which also drops linearity), ridge regression, or simply any degenerate estimator.

Proof Let {estilo de exibição {tilde {beta }}=Cy} be another linear estimator of {beta de estilo de exibição } com {estilo de exibição C=(X'X)^{-1}X'+D} Onde {estilo de exibição D} é um {displaystyle Ktimes n} non-zero matrix. As we're restricting to unbiased estimators, minimum mean squared error implies minimum variance. The goal is therefore to show that such an estimator has a variance no smaller than that of {estilo de exibição {chapéu largo {beta }},} the OLS estimator. We calculate: {estilo de exibição {começar{alinhado}nome do operador {E} deixei[{tilde {beta }}certo]&=operatorname {E} [Cy]\&=operatorname {E} deixei[deixei((X'X)^{-1}X'+Dright)(Xbeta +varepsilon )certo]\&=left((X'X)^{-1}X'+Dright)Xbeta +left((X'X)^{-1}X'+Dright)nome do operador {E} [varepsilon ]\&=left((X'X)^{-1}X'+Dright)Xbeta &&operatorname {E} [varepsilon ]=0\&=(X'X)^{-1}X'Xbeta +DXbeta \&=(EU_{K}+DX)beta .\end{alinhado}}} Portanto, desde {beta de estilo de exibição } is unobservable, {estilo de exibição {tilde {beta }}} is unbiased if and only if {displaystyle DX=0} . Então: {estilo de exibição {começar{alinhado}nome do operador {Era} deixei({tilde {beta }}certo)&=operatorname {Era} (Cy)\&=C{texto{ Era}}(y)C'\&=sigma ^{2}CC'\&=sigma ^{2}deixei((X'X)^{-1}X'+Dright)deixei(X(X'X)^{-1}+D'right)\&=sigma ^{2}deixei((X'X)^{-1}X'X(X'X)^{-1}+(X'X)^{-1}X'D'+DX(X'X)^{-1}+DD'right)\&=sigma ^{2}(X'X)^{-1}+sigma^{2}(X'X)^{-1}(DX)'+sigma ^{2}DX(X'X)^{-1}+sigma^{2}DD'\&=sigma ^{2}(X'X)^{-1}+sigma^{2}DD'&&DX=0\&=operatorname {Era} deixei({chapéu largo {beta }}certo)+sigma^{2}DD'&&sigma ^{2}(X'X)^{-1}=nome do operador {Era} deixei({chapéu largo {beta }}certo)fim{alinhado}}} Since DD' is a positive semidefinite matrix, {nome do operador de estilo de exibição {Era} deixei({tilde {beta }}certo)} exceeds {nome do operador de estilo de exibição {Era} deixei({chapéu largo {beta }}certo)} by a positive semidefinite matrix.

Remarks on the proof As it has been stated before, the condition of {nome do operador de estilo de exibição {Era} deixei({tilde {beta }}certo)-nome do operador {Era} deixei({chapéu largo {beta }}certo)} is a positive semidefinite matrix is equivalent to the property that the best linear unbiased estimator of {displaystyle ell ^{t}beta } é {displaystyle ell ^{t}{chapéu largo {beta }}} (best in the sense that it has minimum variance). Para ver isso, deixar {displaystyle ell ^{t}{tilde {beta }}} another linear unbiased estimator of {displaystyle ell ^{t}beta } .

{estilo de exibição {começar{alinhado}nome do operador {Era} deixei(ell ^{t}{tilde {beta }}certo)&=ell ^{t}nome do operador {Era} deixei({tilde {beta }}certo)ell \&=sigma ^{2}ell ^{t}(X'X)^{-1}ell +ell ^{t}DD^{t}ell \&=operatorname {Era} deixei(ell ^{t}{chapéu largo {beta }}certo)+(D^{t}bem )^{t}(D^{t}bem )&&sigma ^{2}ell ^{t}(X'X)^{-1}ell =operatorname {Era} deixei(ell ^{t}{chapéu largo {beta }}certo)\&=operatorname {Era} deixei(ell ^{t}{chapéu largo {beta }}certo)+|D^{t}bem |\&geq operatorname {Era} deixei(ell ^{t}{chapéu largo {beta }}certo)fim{alinhado}}} Além disso, equality holds if and only if {estilo de exibição D^{t}ell = 0} . We calculate {estilo de exibição {começar{alinhado}ell ^{t}{tilde {beta }}&=ell ^{t}deixei(((X'X)^{-1}X'+D)Yright)&&{texto{ de cima}}\&=ell ^{t}(X'X)^{-1}X'Y+ell ^{t}DY\&=ell ^{t}{chapéu largo {beta }}+(D^{t}bem )^{t}Y\&=ell ^{t}{chapéu largo {beta }}&&D^{t}ell =0end{alinhado}}} This proves that the equality holds if and only if {displaystyle ell ^{t}{tilde {beta }}=ell ^{t}{chapéu largo {beta }}} which gives the uniqueness of the OLS estimator as a BLUE.

Generalized least squares estimator The generalized least squares (GLS), developed by Aitken, extends the Gauss–Markov theorem to the case where the error vector has a non-scalar covariance matrix. The Aitken estimator is also a BLUE.

Gauss–Markov theorem as stated in econometrics In most treatments of OLS, the regressors (parameters of interest) in the design matrix {estilo de exibição mathbf {X} } are assumed to be fixed in repeated samples. This assumption is considered inappropriate for a predominantly nonexperimental science like econometrics. Em vez de, the assumptions of the Gauss–Markov theorem are stated conditional on {estilo de exibição mathbf {X} } .

Linearity The dependent variable is assumed to be a linear function of the variables specified in the model. The specification must be linear in its parameters. This does not mean that there must be a linear relationship between the independent and dependent variables. The independent variables can take non-linear forms as long as the parameters are linear. The equation {displaystyle y=beta _{0}+beta _{1}x^{2},} qualifies as linear while {displaystyle y=beta _{0}+beta _{1}^{2}x} can be transformed to be linear by replacing {estilo de exibição beta _{1}^{2}} by another parameter, dizer {gama de estilo de exibição } . An equation with a parameter dependent on an independent variable does not qualify as linear, por exemplo {displaystyle y=beta _{0}+beta _{1}(x)cdot x} , Onde {estilo de exibição beta _{1}(x)} is a function of {estilo de exibição x} .

Data transformations are often used to convert an equation into a linear form. Por exemplo, the Cobb–Douglas function—often used in economics—is nonlinear: {displaystyle Y=AL^{alfa }K^{1-alfa }e^{varepsilon }} But it can be expressed in linear form by taking the natural logarithm of both sides: {displaystyle ln Y=ln A+alpha ln L+(1-alfa )ln K+varepsilon =beta _{0}+beta _{1}ln L+beta _{2}ln K+varepsilon } This assumption also covers specification issues: assuming that the proper functional form has been selected and there are no omitted variables.

One should be aware, Contudo, that the parameters that minimize the residuals of the transformed equation do not necessarily minimize the residuals of the original equation.

Strict exogeneity For all {estilo de exibição m} observations, the expectation—conditional on the regressors—of the error term is zero: {nome do operador de estilo de exibição {E} [,varepsilon _{eu}midmathbf {X} ]=nome do operador {E} [,varepsilon _{eu}midmathbf {x} _{1},pontos ,mathbf {x} _{n}]=0.} Onde {estilo de exibição mathbf {x} _{eu}={começar{bmatriz}x_{i1}&x_{i2}&cdots &x_{ik}fim{bmatriz}}^{matemática {T}}} is the data vector of regressors for the ith observation, and consequently {estilo de exibição mathbf {X} ={começar{bmatriz}mathbf {x} _{1}^{matemática {T}}&mathbf {x} _{2}^{matemática {T}}&cdots &mathbf {x} _{n}^{matemática {T}}fim{bmatriz}}^{matemática {T}}} is the data matrix or design matrix.

Geometricamente, this assumption implies that {estilo de exibição mathbf {x} _{eu}} e {displaystyle varepsilon _{eu}} are orthogonal to each other, so that their inner product (ou seja, their cross moment) é zero.

{nome do operador de estilo de exibição {E} [,mathbf {x} _{j}cdot varepsilon _{eu},]={começar{bmatriz}nome do operador {E} [,{x}_{j1}cdot varepsilon _{eu},]\nome do operador {E} [,{x}_{j2}cdot varepsilon _{eu},]\vdots \operatorname {E} [,{x}_{jk}cdot varepsilon _{eu},]fim{bmatriz}}= mathbf {0} quadrilátero {texto{para todos }}eu,jin n} This assumption is violated if the explanatory variables are stochastic, for instance when they are measured with error, or are endogenous. Endogeneity can be the result of simultaneity, where causality flows back and forth between both the dependent and independent variable. Instrumental variable techniques are commonly used to address this problem.

Full rank The sample data matrix {estilo de exibição mathbf {X} } must have full column rank.

{nome do operador de estilo de exibição {classificação} (mathbf {X} )=k} Por outro lado {estilo de exibição mathbf {X} 'mathbf {X} } is not invertible and the OLS estimator cannot be computed.

A violation of this assumption is perfect multicollinearity, ou seja. some explanatory variables are linearly dependent. One scenario in which this will occur is called "dummy variable trap," when a base dummy variable is not omitted resulting in perfect correlation between the dummy variables and the constant term. Multicollinearity (as long as it is not "perfect") can be present resulting in a less efficient, but still unbiased estimate. The estimates will be less precise and highly sensitive to particular sets of data. Multicollinearity can be detected from condition number or the variance inflation factor, among other tests.

Spherical errors The outer product of the error vector must be spherical.

{nome do operador de estilo de exibição {E} [,{símbolo em negrito {varepsilon }}{símbolo em negrito {varepsilon ^{matemática {T}}}}midmathbf {X} ]=nome do operador {Era} [,{símbolo em negrito {varepsilon }}midmathbf {X} ]={começar{bmatriz}sigma^{2}&0&cdots &0\0&sigma ^{2}&cdots &0\vdots &vdots &ddots &vdots \0&0&cdots &sigma ^{2}fim{bmatriz}}=sigma ^{2}mathbf {EU} quadrilátero {texto{com }}sigma^{2}>0} This implies the error term has uniform variance (homoscedasticity) and no serial dependence. If this assumption is violated, OLS is still unbiased, but inefficient. The term "spherical errors" will describe the multivariate normal distribution: E se {nome do operador de estilo de exibição {Era} [,{símbolo em negrito {varepsilon }}midmathbf {X} ]=sigma ^{2}mathbf {EU} } in the multivariate normal density, então a equação {estilo de exibição f(varepsilon )=c} is the formula for a ball centered at μ with radius σ in n-dimensional space. Heteroskedasticity occurs when the amount of error is correlated with an independent variable. Por exemplo, in a regression on food expenditure and income, the error is correlated with income. Low income people generally spend a similar amount on food, while high income people may spend a very large amount or as little as low income people spend. Heteroskedastic can also be caused by changes in measurement practices. Por exemplo, as statistical offices improve their data, measurement error decreases, so the error term declines over time.

This assumption is violated when there is autocorrelation. Autocorrelation can be visualized on a data plot when a given observation is more likely to lie above a fitted line if adjacent observations also lie above the fitted regression line. Autocorrelation is common in time series data where a data series may experience "inertia." If a dependent variable takes a while to fully absorb a shock. Spatial autocorrelation can also occur geographic areas are likely to have similar errors. Autocorrelation may be the result of misspecification such as choosing the wrong functional form. In these cases, correcting the specification is one possible way to deal with autocorrelation.

In the presence of spherical errors, the generalized least squares estimator can be shown to be BLUE. See also Independent and identically distributed random variables Linear regression Measurement uncertainty Other unbiased statistics Best linear unbiased prediction (BLUP) Minimum-variance unbiased estimator (MVUE) References ^ See chapter 7 of Johnson, R.A.; Wichern, D.W. (2002). Applied multivariate statistical analysis. Volume. 5. Prentice hall. ^ Theil, Henrique (1971). "Best Linear Unbiased Estimation and Prediction". Principles of Econometrics. Nova york: John Wiley & Sons. pp. 119-124. ISBN 0-471-85845-5. ^ Plackett, R. eu. (1949). "A Historical Note on the Method of Least Squares". Biometrika. 36 (3/4): 458-460. doi:10.2307/2332682. ^ David, F. N.; Neyman, J. (1938). "Extension of the Markoff theorem on least squares". Statistical Research Memoirs. 2: 105-116. OCLC 4025782. ^ Saltar para: a b Aitken, UMA. C. (1935). "On Least Squares and Linear Combinations of Observations". Proceedings of the Royal Society of Edinburgh. 55: 42-48. doi:10.1017/S0370164600014346. ^ Saltar para: a b Huang, David S. (1970). Regression and Econometric Methods. Nova york: John Wiley & Sons. pp. 127-147. ISBN 0-471-41754-8. ^ Hayashi, Fumio (2000). Econometrics. Imprensa da Universidade de Princeton. p. 13. ISBN 0-691-01018-8. ^ Walters, UMA. UMA. (1970). An Introduction to Econometrics. Nova york: C. C. Norton. p. 275. ISBN 0-393-09931-8. ^ Hayashi, Fumio (2000). Econometrics. Imprensa da Universidade de Princeton. p. 7. ISBN 0-691-01018-8. ^ Johnston, John (1972). Econometric Methods (Second ed.). Nova york: McGraw-Hill. pp. 267-291. ISBN 0-07-032679-7. ^ Wooldridge, Jeffrey (2012). Introductory Econometrics (Fifth international ed.). South-Western. p. 220. ISBN 978-1-111-53439-4. ^ Johnston, John (1972). Econometric Methods (Second ed.). Nova york: McGraw-Hill. pp. 159–168. ISBN 0-07-032679-7. ^ Hayashi, Fumio (2000). Econometrics. Imprensa da Universidade de Princeton. p. 10. ISBN 0-691-01018-8. ^ Ramanathan, Ramu (1993). "Nonspherical Disturbances". Statistical Methods in Econometrics. Imprensa Acadêmica. pp. 330–351. ISBN 0-12-576830-3. Further reading Davidson, James (2000). "Statistical Analysis of the Regression Model". Econometric Theory. Oxford: Blackwell. pp. 17–36. ISBN 0-631-17837-6. Goldberger, Arthur (1991). "Classical Regression". A Course in Econometrics. Cambridge: Imprensa da Universidade de Harvard. pp. 160-169. ISBN 0-674-17544-1. Theil, Henrique (1971). "Least Squares and the Standard Linear Model". Principles of Econometrics. Nova york: John Wiley & Sons. pp. 101–162. ISBN 0-471-85845-5. External links Earliest Known Uses of Some of the Words of Mathematics: G (brief history and explanation of the name) Proof of the Gauss Markov theorem for multiple linear regression (makes use of matrix algebra) A Proof of the Gauss Markov theorem using geometry hide vte Least squares and regression analysis Computational statistics Least squaresLinear least squaresNon-linear least squaresIteratively reweighted least squares Correlation and dependence Pearson product-moment correlationRank correlation (Spearman's rhoKendall's tau)Partial correlationConfounding variable Regression analysis Ordinary least squaresPartial least squaresTotal least squaresRidge regression Regression as a statistical model Linear regression Simple linear regressionOrdinary least squaresGeneralized least squaresWeighted least squaresGeneral linear model Predictor structure Polynomial regressionGrowth curve (statistics)Segmented regressionLocal regression Non-standard Nonlinear regressionNonparametricSemiparametricRobustQuantileIsotonic Non-normal errors Generalized linear modelBinomialPoissonLogistic Decomposition of variance Analysis of varianceAnalysis of covarianceMultivariate AOV Model exploration Stepwise regressionModel selection Mallows's CpAICBICModel specificationRegression validation Background Mean and predicted responseGauss–Markov theoremErrors and residualsGoodness of fitStudentized residualMinimum mean-square errorFrisch–Waugh–Lovell theorem Design of experiments Response surface methodologyOptimal designBayesian design Numerical approximation Numerical analysisApproximation theoryNumerical integrationGaussian quadratureOrthogonal polynomialsChebyshev polynomialsChebyshev nodes Applications Curve fittingCalibration curveNumerical smoothing and differentiationSystem identificationMoving least squares Regression analysis categoryStatistics category Mathematics portalStatistics outlineStatistics topics Categories: Theorems in statistics

Se você quiser conhecer outros artigos semelhantes a Gauss–Markov theorem você pode visitar a categoria Theorems in statistics.

Ir para cima

Usamos cookies próprios e de terceiros para melhorar a experiência do usuário Mais informação