analysis in the presence of heteroscedasticity, which led to his formulation of the autoregressive conditional heteroscedasticity (ARCH) modeling technique...
27 KB (3,191 words) - 15:51, 29 May 2024
analysis in the presence of heteroscedasticity, which led to his formulation of the autoregressive conditional heteroscedasticity (ARCH) modeling technique...
5 KB (1,154 words) - 04:37, 9 June 2024
Outline of regression analysis (category Outlines of mathematics and logic)
method Autocorrelation Cointegration Multicollinearity Homoscedasticity and heteroscedasticity Lack of fit Non-normality of errors Outliers Regression...
5 KB (327 words) - 12:15, 30 October 2023
Linear regression (section Heteroscedastic models)
including weighted least squares and the use of heteroscedasticity-consistent standard errors can handle heteroscedasticity in a quite general way. Bayesian...
69 KB (9,635 words) - 07:01, 9 August 2024
either heteroscedasticity, or correlations, or both are present among the error terms of the model, as long as the form of heteroscedasticity and correlation...
34 KB (5,374 words) - 13:41, 18 July 2024
exhibit a phenomenon called heteroscedasticity. If all of the residuals are equal, or do not fan out, they exhibit homoscedasticity. However, a terminological...
16 KB (2,168 words) - 18:00, 25 November 2023
Autoregressive conditional heteroskedasticity (redirect from Autoregressive conditional heteroscedasticity)
Garthoff, R. (2018). "Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity". Spatial Statistics. 26 (1): 125–145. arXiv:1609...
23 KB (3,820 words) - 19:30, 26 May 2024
In statistics, the Glejser test for heteroscedasticity, developed in 1969 by Herbert Glejser, regresses the residuals on the explanatory variable that...
3 KB (352 words) - 08:01, 25 July 2024
Carlos M.; Bera, Anil K. (1980). "Efficient tests for normality, homoscedasticity and serial independence of regression residuals". Economics Letters....
7 KB (901 words) - 07:42, 24 May 2024
Robust regression (section Heteroscedastic errors)
heteroscedasticity. In the homoscedastic model, it is assumed that the variance of the error term is constant for all values of x. Heteroscedasticity...
21 KB (2,642 words) - 07:42, 24 May 2024
regression parameters in the presence of heteroscedastic error terms. In regression analysis, heteroscedasticity refers to unequal variances of the random...
4 KB (661 words) - 14:29, 19 December 2023
Goldfeld–Quandt test (category Statistical deviation and dispersion)
Goldfeld–Quandt test checks for heteroscedasticity in regression analyses. It does this by dividing a dataset into two parts or groups, and hence the test is sometimes...
9 KB (1,012 words) - 16:33, 9 February 2024
unit-treatment additivity) and uses the randomization procedure of the experiment. Both these analyses require homoscedasticity, as an assumption for the...
56 KB (7,597 words) - 13:54, 12 August 2024
Least squares (category Optimization algorithms and methods)
covariance matrix diagonal) may still be unequal (heteroscedasticity). In simpler terms, heteroscedasticity is when the variance of Y i {\displaystyle Y_{i}}...
38 KB (5,515 words) - 16:52, 16 June 2024
Heteroskedasticity-consistent standard errors (redirect from Heteroscedasticity-Consistent Standard Errors)
hdl:10419/189084. Long, J. Scott; Ervin, Laurie H. (2000). "Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model". The American...
18 KB (2,295 words) - 01:13, 20 February 2024
F-test of equality of variances (category Statistical deviation and dispersion)
type I error inflations when conducted as a test of the assumption of homoscedasticity prior to a test of effects.) F-tests for the equality of variances...
6 KB (857 words) - 19:52, 23 January 2023
Pseudo-R-squared (section R2CS by Cox and Snell)
assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression will always be heteroscedastic – the...
8 KB (927 words) - 09:38, 10 July 2024
mean value and lesser in the tails, whereas linear regression assumes homoscedasticity. Secondly, while transformations are available to consider beta distributed...
6 KB (876 words) - 18:31, 13 March 2024
the form and strength of the relationship between the dependent variable and regressors. It might also reveal outliers, heteroscedasticity, and other aspects...
64 KB (8,935 words) - 02:22, 18 May 2024
Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and disease conditions in a defined...
72 KB (8,573 words) - 19:57, 14 August 2024
Data (section Etymology and terminology)
organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used...
21 KB (2,526 words) - 10:26, 28 July 2024
below a score of 4 (five less than 4 and half of the two equal to 4) and 95% are below 7 (nine less than 7 and half of the one equal to 7). Occasionally...
4 KB (558 words) - 13:00, 11 February 2024
Bartlett's test, named after Maurice Stevenson Bartlett, is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances...
4 KB (687 words) - 22:21, 26 April 2024
residuals e i {\displaystyle e_{i}} is constant across observations (homoscedasticity). The residuals e i {\displaystyle e_{i}} are uncorrelated with one...
36 KB (5,081 words) - 16:47, 16 February 2024
that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in 1983. The possibility of bias arises...
20 KB (2,584 words) - 04:42, 4 January 2024
are the five-number summary, sometimes extended to a seven-number summary, and the associated box plot. Entries in an analysis of variance table can also...
6 KB (541 words) - 01:53, 11 January 2024
psychology, and political science. Monte Carlo methods have been recognized as one of the most important and influential ideas of the 20th century, and they...
86 KB (9,805 words) - 16:35, 1 August 2024
used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set...
12 KB (1,623 words) - 20:59, 10 February 2024
Linear discriminant analysis (category CS1 maint: date and year)
simplifying homoscedasticity assumption (i.e. that the class covariances are identical, so Σ 0 = Σ 1 = Σ {\displaystyle \Sigma _{0}=\Sigma _{1}=\Sigma } ) and that...
46 KB (5,986 words) - 20:50, 31 July 2024
Statistical tests are used to test the fit between a hypothesis and the data. Choosing the right statistical test is not a trivial task. The choice of...
12 KB (753 words) - 21:29, 5 July 2024