Properties of estimators using the Univariate Normal model
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Updated
Apr 6, 2023 - R
Properties of estimators using the Univariate Normal model
Unbiasedness of the Sample Variance through a Monte-Carlo simulation.
Sample variance calculator for cosmological observations
Calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
Calculate the variance of a single-precision floating-point strided array using Welford's algorithm.
Calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.
Calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
Compute a corrected sample standard deviation incrementally.
Compute the corrected sample standard deviation over all iterated values.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values.
Calculate the variance of a strided array ignoring NaN values and using a two-pass algorithm.
Calculate the variance of a strided array ignoring NaN values.
Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
Calculate the variance of a double-precision floating-point strided array ignoring NaN values.
Compute a moving variance-to-mean ratio (VMR) incrementally.
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