analysis in the presence of heteroscedasticity, which led to his formulation of the autoregressive conditional heteroscedasticity (ARCH) modeling technique...

Garthoff, R. (2018). "Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity". Spatial Statistics. 26 (1): 125–145. arXiv:1609...

Bartlett's test, and the Brown–Forsythe test. However, when any of these tests are conducted to test the underlying assumption of homoscedasticity (i.e. homogeneity...

heteroscedasticity. In the homoscedastic model, it is assumed that the variance of the error term is constant for all values of x. Heteroscedasticity...

exhibit a phenomenon called heteroscedasticity. If all of the residuals are equal, or do not fan out, they exhibit homoscedasticity. However, a terminological...

Bartlett's test, named after Maurice Stevenson Bartlett, is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances...

analysis in the presence of heteroscedasticity, which led to his formulation of the autoregressive conditional heteroscedasticity (ARCH) modeling technique...

Carlos M.; Bera, Anil K. (1980). "Efficient tests for normality, homoscedasticity and serial independence of regression residuals". Economics Letters....

should be collectively exhaustive and mutually exclusive: every element in the population must be assigned to one and only one stratum. Then sampling is...

fourth spread, or H‑spread. It is defined as the difference between the 75th and 25th percentiles of the data. To calculate the IQR, the data set is divided...

In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter...

descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistics is distinguished from...

with probability p and value 0 with probability q = 1 − p. The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability...

including weighted least squares and the use of heteroscedasticity-consistent standard errors can handle heteroscedasticity in a quite general way. Bayesian...

a test of normality. It was published in 1965 by Samuel Sanford Shapiro and Martin Wilk. The Shapiro–Wilk test tests the null hypothesis that a sample...

specific data set, there are four basic combinations of actual data category and assigned category: true positives TP (correct positive assignments), true...

common measures of central tendency are the arithmetic mean, the median, and the mode. A middle tendency can be calculated for either a finite set of...

models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their...

gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes...

covariance matrix diagonal) may still be unequal (heteroscedasticity). In simpler terms, heteroscedasticity is when the variance of Y i {\displaystyle Y_{i}}...

Herfindahl index Heston model Heteroscedasticity Heteroscedasticity-consistent standard errors Heteroskedasticity – see Heteroscedasticity Hidden Markov model Hidden...

correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1...

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...

distributed under the null hypothesis, specifically Pearson's chi-squared test and variants thereof. Pearson's chi-squared test is used to determine whether...

regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial...

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...

is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables...

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an...

mass, the first moment (normalized by total mass) is the center of mass, and the second moment is the moment of inertia. If the function is a probability...

normal score, standardized variable and pull in high energy physics. Computing a z-score requires knowledge of the mean and standard deviation of the complete...