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In Stata, one specifies the full regression, and then enters the command estat hettest followed by all independent variables. [9] [10] In SAS, Breusch–Pagan can be obtained using the Proc Model option. In Python, there is a method het_breuschpagan in statsmodels.stats.diagnostic (the statsmodels package) for Breusch–Pagan test. [11]
In probability theory, conditional independence describes situations wherein an observation is irrelevant or redundant when evaluating the certainty of a hypothesis. . Conditional independence is usually formulated in terms of conditional probability, as a special case where the probability of the hypothesis given the uninformative observation is equal to the probability
Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector. The number of observations R is assumed to be large, so that in the analysis we take R → ∞ {\displaystyle \infty } , whereas the number of equations m remains fixed.
SAS: The PSMatch procedure, and macro OneToManyMTCH match observations based on a propensity score. [10] Stata: several commands implement propensity score matching, [11] including the user-written psmatch2. [12] Stata version 13 and later also offers the built-in command teffects psmatch. [13]
In words, the observed portion of X should be independent on the missingness status of Y, conditional on every value of Z. Failure to satisfy this condition indicates that the problem belongs to the MNAR category. [22] (Remark: These tests are necessary for variable-based MAR which is a slight variation of event-based MAR. [23] [24] [25])
In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) [1] 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. [2]
In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.
Stata: the command estat dwatson, following regress in time series data. [6] Engle's LM test for autoregressive conditional heteroskedasticity (ARCH), a test for time-dependent volatility, the Breusch–Godfrey test, and Durbin's alternative test for serial correlation are also available.