Results 241 to 250 of about 1,457,940 (269)
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Variance function estimation for immunoassays
Computer Programs in Biomedicine, 1980A computer program is described which implements a recently described [1], modified likelihood method of determining an appropriate weighting function to use when fitting immunoassay dose-response curves. The relationship between the variance of the response and its mean value is assumed to have an exponential form, and the best fit to this model is ...
G M, Raab, R, Thompson, I, McKenzie
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Zero-intelligence realized variance estimation
Finance and Stochastics, 2010This paper compares a comprehensive set of ``second generation'' realized variance measures using a simulated ``zero-intelligence'' order book market. The emphasis of this investigation lies on statistical efficiency, implementation, and robustness. An important aspect of the analysis is the focus on data sampling.
Gatheral, J., Oomen, R.C.A.
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VARIANCE ESTIMATION FOR QUADRATIC STATISTICS
Journal of Time Series Analysis, 1993Abstract. A frequency domain smoothing procedure is presented which provides variance estimates for linear functions of the periodogram. The method provides consistent estimates of the true asymptotic variance, has good small‐sample efficiency properties in the Gaussian case and reflects non‐Gaussian structure.
Smith, B., Field, C.
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M-estimation of wavelet variance
Annals of the Institute of Statistical Mathematics, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mondal, Debashis, Percival, Donald B.
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Estimation of Variance of the Ratio Estimator
Biometrika, 1982SUMMARY A general class of estimators of the variance of the ratio estimator is considered, which includes two standard estimators vo and v2 and approximates another estimator VH suggested by Royall & Eberhardt (1975). Asymptotic expansions for the variances and biases of the proposed estimators are obtained.
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On estimation of variance components*
Statistica Neerlandica, 1980SummaryIn this survey paper the estimation of variance components is given. The least squares approach in variance component estimation is a unifying principle which includes the analysis of variance estimators and the MINQUE. When normality is assumed the maximum likelihood estimators can be used. Many variance component estimators are not permissible
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Variance Estimation with Matched Pairs
Biometrika, 1980SUMMARY We wish to estimate the variance cr2 from n pairs (Xi, Yi) which are independent normal random variables with unknown mean ui and variance a2. According to a marginal or conditional sufficiency argument, all useful informatiQn about U2 iS contained in the differences Xi- Yi. This paper shows that under fairly weak assumptions about the means, a
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