Academic literature on the topic 'Asymptotic variance estimation'

Keen, K. J. Asymptotic variance of the interclass correlation coefficient. University of Toronto, Department of Statistics, 1989.

Cheng, Russell. Standard Asymptotic Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0003.

Mathematical Statistics: Theory and Applications. De Gruyter, 2020.

Gefeller, Olaf, and Franz Woltering. "Asymptotic Variance Estimation of Association Measures: A New Approach to Overcome Computational Problems." In Medical Informatics Europe 1991. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-93503-9_177.

Alzghool, Raed. "ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93726.

Akahira, Masafumi, and Kei Takeuchi. "SECOND ORDER ASYMPTOTIC EFFICIENCY IN TERMS OF THE ASYMPTOTIC VARIANCE OF SEQUENTIAL ESTIMATION PROCEDURES IN THE PRESENCE OF NUISANCE PARAMETERS." In Joint Statistical Papers of Akahira and Takeuchi. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812791221_0034.

"4 Estimating the Variance of the Latent Process." In Asymptotics, Nonparametrics, and Time Series. CRC Press, 1999. http://dx.doi.org/10.1201/9781482269772-22.

"The bootstrap estimator for the asymptotic variance of (/-quantiles." In Exploring Stochastic Laws. De Gruyter, 1995. http://dx.doi.org/10.1515/9783112318768-045.

TAKEUCHI, Kei, and Masafumi AKAHIRA. "SECOND ORDER ASYMPTOTIC EFFICIENCY IN TERMS OF ASYMPTOTIC VARIANCES OF THE SEQUENTIAL MAXIMUM LIKELIHOOD ESTIMATION PROCEDURES." In Joint Statistical Papers of Akahira and Takeuchi. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812791221_0027.

Aïıt-Sahalia, Yacine, and Jean Jacod. "High-Frequency Observations: Identifiability and Asymptotic Efficiency." In High-Frequency Financial Econometrics. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691161433.003.0005.

"Guidance FDA (2001) using a REML UN model. Then, this estimate is asymptotically normally distributed, unbiased with E[νˆ ] = δ +σ − (σ )− 0.04(c ) and has variance of Var[νˆ ] = 4σ δ + l + 2l − 2l + 2l To assess PBE we ‘plug-in’ estimates of δ and the variance components and calculate the upper bound of an asymptotic 90% confidence interval. If this upper bound is below zero we declare that PBE has been shown. Using the code in Appendix B and the data in Section 7.4, we obtain the value −0.24 for log(AUC) and the value −0.19 for log(Cmax). As both of these are below zero, we can declare that T and R are PBE. 7.6 ABE for a replicate design Although ABE can be assessed using a 2× 2 design, it can also be as-sessed using a replicate design. If a replicate design is used the number of subjects can be reduced to up to half that required for a 2 × 2 de-sign. In addition it permits the estimation of σ and σ . The SAS code to assess ABE for a replicate design is given in Appendix B. Using the data from Section 7.4, the 90% confidence interval for µ is (−0.1697,−0.0155) for log(AUC) and (−0.2474,−0.0505) for log(Cmax). Exponentiating the limits to obtain confidence limits for exp(µ ), gives (0.8439,0.9846) for AUC and (0.7808,0.9508) for Cmax. Only the first of these intervals is contained within the limits of 0.8 to 1.25, there-fore T cannot be considered average bioequivalent to R. To calculate the power for a replicate design with four periods and with a total of n subjects we can still use the SAS code given in Section 7.3, if we alter the formula for the variance of a difference of two obser-vations from the same subject. This will now be σ +σ instead of σ , where σ is the subject-by-formulation interaction. Note the use of σ rather than 2σ as used in the RT/TR design. This is a result of the estimator using the average of two measurements on each treatment on each subject. One advantage of using a replicate design is that the number of sub-jects needed can be much smaller than that needed for a 2×2 design. As an example, suppose that σ = 0, and we take σ = 0.355 and α = 0.05, as done in Section 7.3. Then a power of 90.5% can be achieved with only 30 subjects, which is about half the number (58) needed for the 2 × 2 design." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-25.

"strate IBE the upper bound of a 90% confidence interval for the above aggregate metric must fall below 2.49. The required upper bound can be calculated in at least three different ways: (1) method-of-moments estimation with a Cornish-Fisher approx-imation (Hyslop et al., 2000; FDA Guidance, 2001), (2) bootstrapping (FDA Guidance, 1997), and (3) by asymptotic approximations to the mean and variance of ν and ν (Patterson, 2003; Patterson and Jones, 2002b,c). Method (1) derives from theory that assumes the inde-pendence of chi-squared variables and is more appropriate to the analysis of a parallel group design. Hence it does not fully account for the within-subject correlation that is present in data obtained from cross-over tri-als. Moreover, the approach is potentially sensitive to bias introduced by missing data and imbalance in the study data (Patterson and Jones, 2002c). Method (2), which uses the nonparametric percentile bootstrap method (Efron and Tibshirani, 1993), was the earliest suggested method of calculating the upper bound (FDA Guidance, 1997), but it has sev-eral disadvantages. Among these are that it is computationally intensive and it introduces randomness into the final calculated upper bound. Re-cent modifications to ensure consistency of the bootstrap (Shao et al., 2000) do not appear to protect the Type I error rate (Patterson and Jones, 2002c) around the mixed-scaling cut-off (0.04) unless calibration (Efron and Tibshirani, 1993) is used. Use of such a calibration technique is questionable if one is making a regulatory submission. Hence, we pre-fer to use method (3) and will illustrate its use shortly. We note that this method appears to protect against inflation of the Type I error rate in IBE and PBE testing, and the use of REML ensures unbiased esti-mates (Patterson and Jones, 2002c) in data sets with missing data and imbalance, a common occurrence in cross-over designs, (Patterson and Jones, 2002a,b). In general (Patterson and Jones, 2002a), cross-over tri-als that have been used to test for IBE and PBE have used sample sizes in excess of 20 to 30 subjects, so asymptotic testing is not unreasonable, and there is a precedent for the use of such procedures in the study of pharmacokinetics (Machado et al., 1999). We present findings here based on asymptotic normal theory using REML and not taking into account shrinkage (Patterson and Jones, 2002b,c). It is possible to account for this factor using the approach of Harville and Jeske (1992); see also Ken-ward and Roger (1997). However, this approach is not considered here in the interests of space and as the approach described below appears to control the Type I error rate for sample sizes as low as 16 (Patterson and Jones, 2002c). In a 2 × 2 cross-over trial it is not possible to estimate separately the within-and between-subject variances and hence a replicate design, where subjects receiving each formulation more than once is required." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-19.

Anand, G. V., P. V. Nagesha, Sanjeev Gurugopinath, and N. Kalyanasundaram. "Sparse Asymptotic Minimum Variance Bearing Estimation of Underwater Acoustic Sources." In Global Oceans 2020: Singapore - U.S. Gulf Coast. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf38699.2020.9389304.

Dai, Zhiyong, Yanan Qiu, Jianwei Li, and Chao Zhang. "Global asymptotic estimation of grid voltage parameters for variable frequency AC systems." In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8216712.

Balashov, Dmitri, and Horst Irretier. "Maximum Response of a Slow-Variant Mechanical System During Transition Through a Resonance." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8287.

Xiong, Haoyi, Wei Cheng, Yanjie Fu, Wenqing Hu, Jiang Bian, and Zhishan Guo. "De-biasing Covariance-Regularized Discriminant Analysis." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/401.

Mackay, Ed B. L., and Philip Jonathan. "Estimation of Environmental Contours Using a Block Resampling Method." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18308.

Hahn, Jin-Oh, Andrew T. Reisner, and H. Harry Asada. "Identification of Multi-Channel Cardiovascular System Using Dual-Pole Laguerre Basis Functions for Assessment of Aortic Flow and TPR." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41186.

Cruz, Hector L. "Estimating Cooling Towers for Power Plant Applications." In ASME 2006 Power Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/power2006-88191.

Moelling, David, James Malloy, Marc Graham, Mark Taylor, and Andreas Fabricius. "Design Factors for Avoiding FAC Erosion in HRSG Low Pressure Evaporators." In ASME 2013 Power Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/power2013-98213.

Honore, Bo E., and Louija Hu. Easy Bootstrap-Like Estimation of Asymptotic Variances. Federal Reserve Bank of Chicago, 2018. http://dx.doi.org/10.21033/wp-2018-11.

Glynn, Peter W., and Ward Whitt. Estimating the Asymptotic Variance with Batch Means. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada228293.

Kott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.

Hahn, Jinyong, Xiaohong Chen, and Daniel Ackerberg. A practical asymptotic variance estimator for two-step semiparametric estimators. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.2211.