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Theory and Method

On the Distribution of a Linear Combination of Independent Chi Squares

Pages 142-144 | Published online: 05 Apr 2012

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Feng-shou Ko. (2020) An alternate approach for sample size determination in a multi-regional trial. Communications in Statistics - Theory and Methods 49:4, pages 997-1007.
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Amir T. Payandeh Najafabadi & Maryam Omidi Najafabadi. (2016) On the Bayesian estimation for Cronbach's alpha. Journal of Applied Statistics 43:13, pages 2416-2441.
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Feng-Shou Ko. (2016) Sample size determination for quality control process using a multiple components tolerance interval. Communications in Statistics - Theory and Methods 45:12, pages 3668-3674.
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Feng-Shou Ko. (2014) Sample Size Determination for a Two-Sided Multiple Components Tolerance Interval. Communications in Statistics - Theory and Methods 43:24, pages 5241-5248.
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E. J. Allen & V. T. Farewell. (2011) The use of variance components for the assessment of outcome measures in rheumatology. Journal of Applied Statistics 38:1, pages 145-159.
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C Ming Wang. (1994) On estimating approximate degrees of freedom of chi-squared approximations. Communications in Statistics - Simulation and Computation 23:3, pages 769-788.
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C. Ming Wang. (1992) Approximate confidence intervals on linear combinations of expected mean squares. Journal of Statistical Computation and Simulation 43:3-4, pages 229-241.
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C. Ming Wang. (1991) Approximate confidence intervals on positive linear combinations of expected mean squares. Communications in Statistics - Simulation and Computation 20:1, pages 81-96.
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C. Ming Wang. (1988) One-sided confidence intervals for the positive linear combination of two variances. Communications in Statistics - Simulation and Computation 17:1, pages 283-292.
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FranklinA. Graybill & Chih-Ming Wang. (1980) Confidence Intervals on Nonnegative Linear Combinations of Variances. Journal of the American Statistical Association 75:372, pages 869-873.
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Carlos A. Coelho. 2020. Computational and Methodological Statistics and Biostatistics. Computational and Methodological Statistics and Biostatistics 211 252 .
Brian H. Willis & Richard D. Riley. (2017) Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice. Statistics in Medicine 36:21, pages 3283-3301.
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Zhongshang Yuan, Jiadong Ji, Xiaoshuai Zhang, Jing Xu, Daoxin Ma & Fuzhong Xue. (2016) A powerful weighted statistic for detecting group differences of directed biological networks. Scientific Reports 6:1.
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Johannes Bausch. (2013) On the efficient calculation of a linear combination of chi-square random variables with an application in counting string vacua. Journal of Physics A: Mathematical and Theoretical 46:50, pages 505202.
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Hardeo Sahai & Mario Miguel OjedaHardeo Sahai & Mario Miguel Ojeda. 2004. Analysis of Variance for Random Models. Analysis of Variance for Random Models 21 114 .
Nicolaas (Klaas) M. Faber. (1999) Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometrics and Intelligent Laboratory Systems 49:1, pages 79-89.
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Eduardo Bearzoti & Roland Vencovsky. (1998) Estimation of the proportion of genetic variance explained by molecular markers. Genetics and Molecular Biology 21:4, pages 557-566.
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Shayle R. Searle, George Casella & Charles E. McCulloch. 1992. Variance Components. Variance Components 475 489 .
Marsha L. Schroeder & A. Ralph Hakstian. (1990) Inferential procedures for multifaceted coefficients of generalizability. Psychometrika 55:3, pages 429-447.
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Tai-Fang C. Lu, Franklin A. Graybill & Richard K. Burdick. (1988) Confidence intervals on a difference of expected mean squares. Journal of Statistical Planning and Inference 18:1, pages 35-43.
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Joseph L. Fleiss & Patrick E. Shrout. (1978) Approximate interval estimation for a certain intraclass correlation coefficient. Psychometrika 43:2, pages 259-262.
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Govind S. Mudholkar & Siddhartha R. Dalal. (1977) Some bounds on the distribution functions of linear combinations and applications. Annals of the Institute of Statistical Mathematics 29:1, pages 89-100.
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