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Manlai Tang - One of the best experts on this subject based on the ideXlab platform.
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Wiley StatsRef: Statistics Reference Online - Sample Size Determination for Clinical Trials
Wiley StatsRef: Statistics Reference Online, 2014Co-Authors: Manlai TangAbstract:In this article, we will present some useful sample size formulas that guarantee to achieve a pre-specified power level of a test at a chosen nominal level or control a pre-specified Confidence width of a Confidence Interval Estimator at a chosen coverage level (wherever available) for some popular topics in general clinical trials. Topics include: (a) one-sample problem for mean and proportion; (b) two-sample problem for comparing means and proportions; (c) multiple-sample problem for comparing means and proportions; (d) multiple-arm dose response trials for trend; (e) multiple regression analysis; and (f) multiple logistic regression analysis. Keywords: Confidence Interval; Confidence width; hypothesis testing; parameter; power; sample size formulas
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a comparative study of Confidence Intervals for negative binomial proportion
Journal of Statistical Computation and Simulation, 2009Co-Authors: Mao Zai Tian, Hon Keung Tony Ng, Manlai Tang, Ping Shing ChanAbstract:In this paper, we investigate four existing and three new Confidence Interval Estimators for the negative binomial proportion (i.e., proportion under inverse/negative binomial sampling). An extensive and systematic comparative study among these Confidence Interval Estimators through Monte Carlo simulations is presented. The performance of these Confidence Intervals are evaluated in terms of their coverage probabilities and expected Interval widths. Our simulation studies suggest that the Confidence Interval Estimator based on saddlepoint approximation is more appealing for large coverage levels (e.g., nominal level≤1% ) whereas the score Confidence Interval Estimator is more desirable for those commonly used coverage levels (e.g., nominal level>1% ). We illustrate these Confidence Interval construction methods with a real data set from a maternal congenital heart disease study.
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Asymptotic Confidence Interval construction for risk difference under inverse sampling
Computational Statistics & Data Analysis, 2009Co-Authors: Manlai Tang, Mao Zai TianAbstract:Risk difference (RD) has played an important role in a lot of biological and epidemiological investigations to compare the risks of developing certain disease or tumor for two drugs or treatments. When the disease is rare and acute, inverse sampling (rather than binomial sampling) is usually recommended to collect the binary outcomes. In this paper, we derive an asymptotic Confidence Interval Estimator for RD based on the score statistic. To compare its performance with three existing Confidence Interval Estimators, we employ Monte Carlo simulation to evaluate their coverage probabilities, expected Confidence Interval widths, and the mean difference of the coverage probabilities from the nominal Confidence level. Our simulation results suggest that the score-test-based Confidence Interval Estimator is generally more appealing than the Wald, uniformly minimum variance unbiased Estimator and likelihood ratio Confidence Interval Estimators for it maintains the coverage probability close to the desired Confidence level and yields the shortest expected width in most cases. We illustrate these Confidence Interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.
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Confidence Interval for Rate Ratio in a 2 × 2 Table with Structural Zero: An Application in Assessing False‐Negative Rate Ratio When Combining Two Diagnostic Tests
Biometrics, 2004Co-Authors: Manlai Tang, Niansheng Tang, Vincent J CareyAbstract:Summary. In this article, we consider problems with correlated data that can be summarized in a 2 × 2 table with structural zero in one of the off-diagonal cells. Data of this kind sometimes appear in infectious disease studies and two-step procedure studies. Lui (1998, Biometrics54, 706–711) considered Confidence Interval estimation of rate ratio based on Fieller-type, Wald-type, and logarithmic transformation statistics. We reexamine the same problem under the context of Confidence Interval construction on false-negative rate ratio in diagnostic performance when combining two diagnostic tests. We propose a score statistic for testing the null hypothesis of nonunity false-negative rate ratio. Score test–based Confidence Interval construction for false-negative rate ratio will also be discussed. Simulation studies are conducted to compare the performance of the new derived score test statistic and existing statistics for small to moderate sample sizes. In terms of Confidence Interval construction, our asymptotic score test–based Confidence Interval Estimator possesses significantly shorter expected width with coverage probability being close to the anticipated Confidence level. In terms of hypothesis testing, our asymptotic score test procedure has actual type I error rate close to the pre-assigned nominal level. We illustrate our methodologies with real examples from a clinical laboratory study and a cancer study.
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Confidence Interval for rate ratio in a 2 2 table with structural zero an application in assessing false negative rate ratio when combining two diagnostic tests
Biometrics, 2004Co-Authors: Manlai Tang, Niansheng Tang, Vincent J CareyAbstract:Summary. In this article, we consider problems with correlated data that can be summarized in a 2 × 2 table with structural zero in one of the off-diagonal cells. Data of this kind sometimes appear in infectious disease studies and two-step procedure studies. Lui (1998, Biometrics54, 706–711) considered Confidence Interval estimation of rate ratio based on Fieller-type, Wald-type, and logarithmic transformation statistics. We reexamine the same problem under the context of Confidence Interval construction on false-negative rate ratio in diagnostic performance when combining two diagnostic tests. We propose a score statistic for testing the null hypothesis of nonunity false-negative rate ratio. Score test–based Confidence Interval construction for false-negative rate ratio will also be discussed. Simulation studies are conducted to compare the performance of the new derived score test statistic and existing statistics for small to moderate sample sizes. In terms of Confidence Interval construction, our asymptotic score test–based Confidence Interval Estimator possesses significantly shorter expected width with coverage probability being close to the anticipated Confidence level. In terms of hypothesis testing, our asymptotic score test procedure has actual type I error rate close to the pre-assigned nominal level. We illustrate our methodologies with real examples from a clinical laboratory study and a cancer study.
Ho-chang Kuo - One of the best experts on this subject based on the ideXlab platform.
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Prognostic nutrition index as a predictor of coronary artery aneurysm in Kawasaki Disease
BMC Pediatrics, 2020Co-Authors: I-hsin Tai, Mindy Ming-huey Guo, Jessica Lee, Chi-hsiang Chu, Kai-sheng Hsieh, Ho-chang KuoAbstract:Background Kawasaki Disease (KD) is considered a major acquired heart disease in children under the age of 5. Coronary artery aneurysm (CAA) can occur in serious cases despite extreme therapy efforts. Previous studies have reported low serum albumin level was associated with disease outcome, but no further investigation was addressed yet. Method This retrospective (case-control) study randomly included children with KD who were admitted and underwent laboratory tests before undergoing IVIG treatment in this institution, the largest tertiary medical center in southern Taiwan from 2012 to 2016. Prognostic nutrition index (PNI), an albumin-based formula product, was evaluated as a predictor of CAA the first time. The progression of CAA was monitored using serial echocardiography for six months. We performed multivariable logistic regression analysis on the laboratory test and PNI with the disease outcome of the KD patients. Result Of the 275 children, 149 had CAA, including transient dilatation, while the other 126 did not develop CAA during the 6-month follow-up period. A multivariate logistic regression model revealed that PNI, gender, IVIG non-responder, and platelet count are significant predictors of CAA with a 95% Confidence Interval Estimator of 1.999, 3.058, 3.864 and 1.004, respectively. Using PNI to predict CAA presence gave an area under the receiver-operating-characteristics (ROC) curve of 0.596. For a cutoff of 0.5 in the logistic regression model and the PNI cut-off point is taken as 55 together with IVIG non-responder, boy gender, and platelet count take into account, sensitivity and specificity were 65.7 and 70.4%. Conclusion PNI could be a candidate of adjunctive predictor of coronary artery aneurysm in addition to IVIG non-responder. Together with low PNI, IVIG non-responder, male gender and platelet count will give high odds to predict coronary artery aneurysm within 6 months of illness.
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Prognostic nutrition index as a predictor of coronary artery aneurysm in Kawasaki Disease
2020Co-Authors: I-hsin Tai, Mindy Ming-huey Guo, Jessica Lee, Chi-hsiang Chu, Kai-sheng Hsieh, Ho-chang KuoAbstract:Abstract Background: Kawasaki Disease (KD) is considered a major acquired heart disease in children under the age of 5. Coronary artery aneurysm (CAA) can occur in serious cases despite extreme therapy efforts. Previous studies have reported low serum albumin level was associated with disease outcome, but no further investigation was addressed yet. Method: This retrospective (case-control) study randomly included children with KD who were admitted and underwent laboratory tests before undergoing IVIG treatment in this institution, the largest tertiary medical center in southern Taiwan from 2012-2016. Prognostic nutrition index (PNI), an albumin-based formula product, was evaluated as a predictor of CAA the first time. The progression of CAA was monitored using serial echocardiography for six months. We performed multivariable logistic regression analysis on the laboratory test and PNI with the disease outcome of the KD patients. Result: Of the 275 children, 149 had CAA, including transient dilatation, while the other 126 did not develop CAA during the 6-month follow-up period. A multivariate logistic regression model revealed that PNI, gender, IVIG non-responder, and platelet count are significant predictors of CAA with a 95% Confidence Interval Estimator of 1.999, 3.058, 3.864 and1.004, respectively. Using PNI to predict CAA presence gave an area under the receiver-operating-characteristics (ROC) curve of 0.596. For a cutoff of 0.5 in the logistic regression model and the PNI cut-off point is taken as 55 together with IVIG non-responder, boy gender, and platelet count take into account, sensitivity and specificity were 65.7% and 70.4%. Conclusion: PNI could be a candidate of adjunctive predictor of coronary artery aneurysm in addition to IVIG non-responder. Together with low PNI, IVIG non-responder, male gender and platelet count will give high odds to predict coronary artery aneurysm within 6 months of illness.
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Prognostic nutrition index as a predictor of coronary artery lesions in Kawasaki Disease
2020Co-Authors: I-hsin Tai, Mindy Ming-huey Guo, Jessica Lee, Chi-hsiang Chu, Kai-sheng Hsieh, Ho-chang KuoAbstract:Abstract Background: Kawasaki Disease (KD) is considered a major acquired heart disease in children under the age of 5. Coronary artery lesions (CAL) can occur in serious cases despite extreme therapy efforts. Previous studies have reported low serum albumin level was associated with disease outcome, but no further investigation was addressed yet. Method: This retrospective (case-control) study randomly included children with KD who were admitted and underwent laboratory tests before undergoing IVIG treatment in this institution, the largest tertiary medical center in southern Taiwan from 2012-2016. PNI, an albumin-based formula product, was evaluated as a predictor of CAL the first time. The progression of CAL was monitored using serial echocardiography for six months. We performed multivariable logistic regression analysis on the laboratory test and PNI with the disease outcome of the KD patients. Result: Of the 284 children, 158 had CAL, including transient dilatation, while the other 126 did not develop CAL during the 6-month follow-up period. A multivariate logistic regression model revealed that PNI and platelet count are significant predictors of CAL with a 95% Confidence Interval Estimator of 2.532 (1.394-4.599) and1.004 (1.002-1.006), respectively. Using PNI to predict CAL presence gave an area under the receiver-operating-characteristics (ROC) curve of 0.596, and the PNI cut-off point is taken as 55.24, with a sensitivity of 0.509 and specificity of 0.678. Conclusion: This is the first study to demonstrate that PNI, an albumin-based formula product, is a useful index with clearly cut-off value for predicting CAL formation prior to initial IVIG therapy and thus warn clinicians to adopt aggressive therapeutic and coronary arteries imaging surveillance strategies before CAL can develop.
James R. Wilson - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - SBatch: a spaced batch means procedure for simulation analysis
2007 Winter Simulation Conference, 2007Co-Authors: Emily K. Lada, James R. WilsonAbstract:We discuss SBatch, a simplified procedure for steady-state simulation analysis that is based on spaced batch means, incorporating many advantages of its predecessors ASAP3 and WASSP while avoiding many of their disadvantages. SBatch is a sequential procedure designed to produce a Confidence- Interval Estimator for the steady-state mean response that satisfies user-specified precision and coverage-probability requirements. First SBatch determines a batch size and an interbatch spacer size such that beyond the initial spacer, the spaced batch means approximately form a stationary first-order autoregressive process whose lag-one correlation does not significantly exceed 0.8. Next SBatch delivers a correlation-adjusted Confidence Interval based on the sample variance and lag-one correlation of the spaced batch means as well as the grand mean of all the individual observations beyond the initial spacer. In an experimental evaluation, SBatch compared favorably with ASAP3 and WASSP.
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Performance evaluation of recent procedures for steady-state simulation analysis
IIE Transactions, 2006Co-Authors: Emily K. Lada, Natalie M. Steiger, James R. WilsonAbstract:The performance of the batch-means procedure ASAP3 and the spectral procedure WASSP is evaluated on test problems with characteristics typical of practical applications of steady-state simulation analysis procedures. ASAP3 and WASSP are sequential procedures designed to produce a Confidence-Interval Estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. ASAP3 is based on an inverse Cornish-Fisher expansion for the classical batch-means t-ratio, whereas WASSP is based on a wavelet Estimator of the batch-means power spectrum. Regarding closeness of the empirical coverage probability and average half-length of the delivered Confidence Intervals to their respective nominal levels, both procedures compared favorably with the Law-Carson procedure and the original ASAP algorithm. Regarding the average sample sizes required for decreasing levels of maximum Confidence-Interval half-length, ASAP3 and WASSP exhibited reasonable efficiency in the test problems.
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Winter Simulation Conference - Performance evaluation of spectral procedures for simulation analysis
Proceedings of the 2006 Winter Simulation Conference, 2006Co-Authors: Emily K. Lada, James R. WilsonAbstract:We summarize an experimental performance evaluation of WASSP and the Heidelberger-Welch (HW) algorithm, two sequential spectral procedures for steady-state simulation analysis. Both procedures approximate the log-smoothed-periodogram of the batch means after suitable data-truncation to eliminate the effects of initialization bias, finally delivering a Confidence-Interval Estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. HW uses a Cramer-von Mises test for initialization bias based on the method of standardized time series; and then HW fits a quadratic polynomial to the batch-means log-spectrum. In contrast WASSP uses the von Neumann randomness test and the Shapiro-Wilk normality test to obtain an approximately stationary Gaussian batch-means process whose log-spectrum is approximated via wavelets. Moreover, unlike HW, WASSP estimates the final sample size required to satisfy the user's Confidence-Interval requirements. Regarding closeness of conformance to both Confidence-Interval requirements, we found that WASSP outperformed HW in the given test problems.
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ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation, 2005Co-Authors: Natalie M. Steiger, Christos Alexopoulos, James R. Wilson, Emily K. Lada, Jeffrey A. Joines, David GoldsmanAbstract:We introduce ASAP3, a refinement of the batch means algorithms ASAP and ASAP2, that delivers point and Confidence-Interval Estimators for the expected response of a steady-state simulation. ASAP3 is a sequential procedure designed to produce a Confidence-Interval Estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t-ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted Confidence Interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered Confidence Interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.
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Winter Simulation Conference - Simulation output analysis: a wavelet-based spectral method for steady-state simulation analysis
2003Co-Authors: Emily K. Lada, James R. Wilson, Natalie M. SteigerAbstract:We develop an automated wavelet-based spectral method for constructing an approximate Confidence Interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wavelets to estimate the batch means log-spectrum and ultimately the steady-state variance constant (SSVC) of the original (unbatched) process. WASSP combines the SSVC Estimator with the grand average of the batch means in a sequential procedure for constructing a Confidence-Interval Estimator of the steady-state mean that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. An extensive performance evaluation provides evidence of WASSP's robustness in comparison with some other output analysis methods.
Mao Zai Tian - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of Confidence Intervals for negative binomial proportion
Journal of Statistical Computation and Simulation, 2009Co-Authors: Mao Zai Tian, Hon Keung Tony Ng, Manlai Tang, Ping Shing ChanAbstract:In this paper, we investigate four existing and three new Confidence Interval Estimators for the negative binomial proportion (i.e., proportion under inverse/negative binomial sampling). An extensive and systematic comparative study among these Confidence Interval Estimators through Monte Carlo simulations is presented. The performance of these Confidence Intervals are evaluated in terms of their coverage probabilities and expected Interval widths. Our simulation studies suggest that the Confidence Interval Estimator based on saddlepoint approximation is more appealing for large coverage levels (e.g., nominal level≤1% ) whereas the score Confidence Interval Estimator is more desirable for those commonly used coverage levels (e.g., nominal level>1% ). We illustrate these Confidence Interval construction methods with a real data set from a maternal congenital heart disease study.
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Asymptotic Confidence Interval construction for risk difference under inverse sampling
Computational Statistics & Data Analysis, 2009Co-Authors: Manlai Tang, Mao Zai TianAbstract:Risk difference (RD) has played an important role in a lot of biological and epidemiological investigations to compare the risks of developing certain disease or tumor for two drugs or treatments. When the disease is rare and acute, inverse sampling (rather than binomial sampling) is usually recommended to collect the binary outcomes. In this paper, we derive an asymptotic Confidence Interval Estimator for RD based on the score statistic. To compare its performance with three existing Confidence Interval Estimators, we employ Monte Carlo simulation to evaluate their coverage probabilities, expected Confidence Interval widths, and the mean difference of the coverage probabilities from the nominal Confidence level. Our simulation results suggest that the score-test-based Confidence Interval Estimator is generally more appealing than the Wald, uniformly minimum variance unbiased Estimator and likelihood ratio Confidence Interval Estimators for it maintains the coverage probability close to the desired Confidence level and yields the shortest expected width in most cases. We illustrate these Confidence Interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.
Emily K. Lada - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - SBatch: a spaced batch means procedure for simulation analysis
2007 Winter Simulation Conference, 2007Co-Authors: Emily K. Lada, James R. WilsonAbstract:We discuss SBatch, a simplified procedure for steady-state simulation analysis that is based on spaced batch means, incorporating many advantages of its predecessors ASAP3 and WASSP while avoiding many of their disadvantages. SBatch is a sequential procedure designed to produce a Confidence- Interval Estimator for the steady-state mean response that satisfies user-specified precision and coverage-probability requirements. First SBatch determines a batch size and an interbatch spacer size such that beyond the initial spacer, the spaced batch means approximately form a stationary first-order autoregressive process whose lag-one correlation does not significantly exceed 0.8. Next SBatch delivers a correlation-adjusted Confidence Interval based on the sample variance and lag-one correlation of the spaced batch means as well as the grand mean of all the individual observations beyond the initial spacer. In an experimental evaluation, SBatch compared favorably with ASAP3 and WASSP.
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Performance evaluation of recent procedures for steady-state simulation analysis
IIE Transactions, 2006Co-Authors: Emily K. Lada, Natalie M. Steiger, James R. WilsonAbstract:The performance of the batch-means procedure ASAP3 and the spectral procedure WASSP is evaluated on test problems with characteristics typical of practical applications of steady-state simulation analysis procedures. ASAP3 and WASSP are sequential procedures designed to produce a Confidence-Interval Estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. ASAP3 is based on an inverse Cornish-Fisher expansion for the classical batch-means t-ratio, whereas WASSP is based on a wavelet Estimator of the batch-means power spectrum. Regarding closeness of the empirical coverage probability and average half-length of the delivered Confidence Intervals to their respective nominal levels, both procedures compared favorably with the Law-Carson procedure and the original ASAP algorithm. Regarding the average sample sizes required for decreasing levels of maximum Confidence-Interval half-length, ASAP3 and WASSP exhibited reasonable efficiency in the test problems.
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Winter Simulation Conference - Performance evaluation of spectral procedures for simulation analysis
Proceedings of the 2006 Winter Simulation Conference, 2006Co-Authors: Emily K. Lada, James R. WilsonAbstract:We summarize an experimental performance evaluation of WASSP and the Heidelberger-Welch (HW) algorithm, two sequential spectral procedures for steady-state simulation analysis. Both procedures approximate the log-smoothed-periodogram of the batch means after suitable data-truncation to eliminate the effects of initialization bias, finally delivering a Confidence-Interval Estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. HW uses a Cramer-von Mises test for initialization bias based on the method of standardized time series; and then HW fits a quadratic polynomial to the batch-means log-spectrum. In contrast WASSP uses the von Neumann randomness test and the Shapiro-Wilk normality test to obtain an approximately stationary Gaussian batch-means process whose log-spectrum is approximated via wavelets. Moreover, unlike HW, WASSP estimates the final sample size required to satisfy the user's Confidence-Interval requirements. Regarding closeness of conformance to both Confidence-Interval requirements, we found that WASSP outperformed HW in the given test problems.
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ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation, 2005Co-Authors: Natalie M. Steiger, Christos Alexopoulos, James R. Wilson, Emily K. Lada, Jeffrey A. Joines, David GoldsmanAbstract:We introduce ASAP3, a refinement of the batch means algorithms ASAP and ASAP2, that delivers point and Confidence-Interval Estimators for the expected response of a steady-state simulation. ASAP3 is a sequential procedure designed to produce a Confidence-Interval Estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t-ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted Confidence Interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered Confidence Interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.
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Winter Simulation Conference - Simulation output analysis: a wavelet-based spectral method for steady-state simulation analysis
2003Co-Authors: Emily K. Lada, James R. Wilson, Natalie M. SteigerAbstract:We develop an automated wavelet-based spectral method for constructing an approximate Confidence Interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wavelets to estimate the batch means log-spectrum and ultimately the steady-state variance constant (SSVC) of the original (unbatched) process. WASSP combines the SSVC Estimator with the grand average of the batch means in a sequential procedure for constructing a Confidence-Interval Estimator of the steady-state mean that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. An extensive performance evaluation provides evidence of WASSP's robustness in comparison with some other output analysis methods.