Pub Date : 2022-10-02DOI: 10.1080/07474946.2022.2129690
S. Efromovich
Abstract The article considers an adaptive sequential nonparametric estimation of a multivariate regression with assigned mean integrated squared error (MISE) and minimax mean stopping time when the estimator matches performance of an oracle knowing all nuisance parameters and functions. It is known that the problem has no solution if regression belongs to a Sobolev class of differentiable functions. What if an underlying regression is smoother, say, analytic? It is shown that in this case it is possible to match performance of the oracle. Furthermore, similar to the classical Stein solution for a parameter estimation, a two-stage sequential procedure solves the problem. The proposed regression estimator for the first stage, based on a sample with fixed sample size, is of interest on its own, and a thought-provoking environmental example of reducing potent greenhouse gas emission by an anaerobic digestion system is used to discuss a number of important topics for small samples.
{"title":"Sequential nonparametric estimation of controlled multivariate regression","authors":"S. Efromovich","doi":"10.1080/07474946.2022.2129690","DOIUrl":"https://doi.org/10.1080/07474946.2022.2129690","url":null,"abstract":"Abstract The article considers an adaptive sequential nonparametric estimation of a multivariate regression with assigned mean integrated squared error (MISE) and minimax mean stopping time when the estimator matches performance of an oracle knowing all nuisance parameters and functions. It is known that the problem has no solution if regression belongs to a Sobolev class of differentiable functions. What if an underlying regression is smoother, say, analytic? It is shown that in this case it is possible to match performance of the oracle. Furthermore, similar to the classical Stein solution for a parameter estimation, a two-stage sequential procedure solves the problem. The proposed regression estimator for the first stage, based on a sample with fixed sample size, is of interest on its own, and a thought-provoking environmental example of reducing potent greenhouse gas emission by an anaerobic digestion system is used to discuss a number of important topics for small samples.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"492 - 511"},"PeriodicalIF":0.8,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41637008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/07474946.2022.2074453
E. Mahmoudi, Zahra Nemati, Ashkan Khalifeh
Abstract In this article, the two-stage sequential sampling procedure is proposed to estimate the mean of an exponential distribution under the modified square error loss function. The main aim of the article is to consider the associated risk per unit cost function by bounding it from above with a fixed preassigned positive number, ω. We provide the exact distribution of the total sample size, explicit formulas for the mathematical expectation of the rth power of the total sample size, the expected value and mean squared error of the maximum likelihood estimate of the scale, and mean parameters of the exponential distribution under the two-stage sequential procedure. The performances of the proposed methodologies are investigated with the help of simulations. In the end, we supplement with extensive sets of data analysis via computer simulations, validating that the two-stage sequential method performs very well. Finally, using a real data set, the procedure is clearly illustrated.
{"title":"Bounded risk per unit cost index constraint for sequential estimation of the mean in a two-parameter exponential distribution","authors":"E. Mahmoudi, Zahra Nemati, Ashkan Khalifeh","doi":"10.1080/07474946.2022.2074453","DOIUrl":"https://doi.org/10.1080/07474946.2022.2074453","url":null,"abstract":"Abstract In this article, the two-stage sequential sampling procedure is proposed to estimate the mean of an exponential distribution under the modified square error loss function. The main aim of the article is to consider the associated risk per unit cost function by bounding it from above with a fixed preassigned positive number, ω. We provide the exact distribution of the total sample size, explicit formulas for the mathematical expectation of the rth power of the total sample size, the expected value and mean squared error of the maximum likelihood estimate of the scale, and mean parameters of the exponential distribution under the two-stage sequential procedure. The performances of the proposed methodologies are investigated with the help of simulations. In the end, we supplement with extensive sets of data analysis via computer simulations, validating that the two-stage sequential method performs very well. Finally, using a real data set, the procedure is clearly illustrated.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"285 - 309"},"PeriodicalIF":0.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44543720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/07474946.2022.2096900
Srawan Kumar Bishnoi, N. Mukhopadhyay
Abstract We develop a new class of purely sequential methodologies under an assumption that the population distribution belongs to a location-scale family. Both asymptotic first-order and second-order theories are put forward with substantial generality under a big and unified tent that successfully lead to a broad set of illustrations. After we identify an appropriately defined optimal strategy under this unified structure, we introduce applications that handle a variety of interesting inference problems. These are associated with, but not limited to, the following areas: (a) the fixed-width confidence interval (FWCI) estimation, (b) the minimum risk point estimation (MRPE), (c) the fixed-size confidence region (FSCR) estimation, (d) multiple comparisons, and (e) selecting the best normal treatment (StBNT). In illustrations (a)–(d), we have highlighted a number of choices of population distributions. Some illustrations are accompanied with data analyses.
{"title":"An optimal purely sequential strategy with asymptotic second-order properties: Applications from statistical inference and data analysis","authors":"Srawan Kumar Bishnoi, N. Mukhopadhyay","doi":"10.1080/07474946.2022.2096900","DOIUrl":"https://doi.org/10.1080/07474946.2022.2096900","url":null,"abstract":"Abstract We develop a new class of purely sequential methodologies under an assumption that the population distribution belongs to a location-scale family. Both asymptotic first-order and second-order theories are put forward with substantial generality under a big and unified tent that successfully lead to a broad set of illustrations. After we identify an appropriately defined optimal strategy under this unified structure, we introduce applications that handle a variety of interesting inference problems. These are associated with, but not limited to, the following areas: (a) the fixed-width confidence interval (FWCI) estimation, (b) the minimum risk point estimation (MRPE), (c) the fixed-size confidence region (FSCR) estimation, (d) multiple comparisons, and (e) selecting the best normal treatment (StBNT). In illustrations (a)–(d), we have highlighted a number of choices of population distributions. Some illustrations are accompanied with data analyses.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"325 - 366"},"PeriodicalIF":0.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42588597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/07474946.2022.2092137
Marlo Brown
Abstract We look at a sequence of Bernoulli random variables where the success rates change from θ 1 to θ 2. We will assume that both the success rates before and after the change are known. We also assume that this change does not happen abruptly but gradually over a period of time η where η is known. We calculate the probability that the change has started and completed. We also look at optimal stopping rules assuming that there is a cost for a false alarm and a cost per time unit to stop early.
{"title":"Monitoring a Bernoulli process subject to gradual changes in the success rates of a sequence of Bernoulli random variables","authors":"Marlo Brown","doi":"10.1080/07474946.2022.2092137","DOIUrl":"https://doi.org/10.1080/07474946.2022.2092137","url":null,"abstract":"Abstract We look at a sequence of Bernoulli random variables where the success rates change from θ 1 to θ 2. We will assume that both the success rates before and after the change are known. We also assume that this change does not happen abruptly but gradually over a period of time η where η is known. We calculate the probability that the change has started and completed. We also look at optimal stopping rules assuming that there is a cost for a false alarm and a cost per time unit to stop early.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"310 - 324"},"PeriodicalIF":0.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59425376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/07474946.2022.2105361
M. Alam, Shantonu Islam Shanto
Abstract This article incorporates a covariate to determine the optimum dose in a seamless phase I/II clinical trial. A binary covariate and its interaction effect are assumed to keep the method simple. Each patient’s outcome is assumed to be trinomial, and the continuation ratio model is utilized to model the dose–response data. The Bayesian approach estimates parameters of the dose–response model. Eight plausible dose–response scenarios are investigated to check the proposed methodology. A simulation study shows that covariate consideration can enhance the identification of the optimum dose when it is appropriate to do.
{"title":"Patient-specific dose finding in seamless phase I/II clinical trials","authors":"M. Alam, Shantonu Islam Shanto","doi":"10.1080/07474946.2022.2105361","DOIUrl":"https://doi.org/10.1080/07474946.2022.2105361","url":null,"abstract":"Abstract This article incorporates a covariate to determine the optimum dose in a seamless phase I/II clinical trial. A binary covariate and its interaction effect are assumed to keep the method simple. Each patient’s outcome is assumed to be trinomial, and the continuation ratio model is utilized to model the dose–response data. The Bayesian approach estimates parameters of the dose–response model. Eight plausible dose–response scenarios are investigated to check the proposed methodology. A simulation study shows that covariate consideration can enhance the identification of the optimum dose when it is appropriate to do.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"367 - 386"},"PeriodicalIF":0.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41818334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/07474946.2022.2108546
Peiyao Wang, Wei Ning
Abstract In this article, we propose a modified max-cumulative sum (CUSUM) procedure for detecting changes in parameters of skew normal distribution. The corresponding false alarms frequency and the postchange detection delay are investigated. Asymptotic behaviors of detection delay and theoretical optimality of the detection procedure have been established. Simulations have been conducted to show the performance of the proposed method and compare it to the other existing methods including CUSUM. Real data are given to illustrate the detection procedure.
{"title":"Sequential change-point detection for skew normal distribution","authors":"Peiyao Wang, Wei Ning","doi":"10.1080/07474946.2022.2108546","DOIUrl":"https://doi.org/10.1080/07474946.2022.2108546","url":null,"abstract":"Abstract In this article, we propose a modified max-cumulative sum (CUSUM) procedure for detecting changes in parameters of skew normal distribution. The corresponding false alarms frequency and the postchange detection delay are investigated. Asymptotic behaviors of detection delay and theoretical optimality of the detection procedure have been established. Simulations have been conducted to show the performance of the proposed method and compare it to the other existing methods including CUSUM. Real data are given to illustrate the detection procedure.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"387 - 415"},"PeriodicalIF":0.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/07474946.2022.2164307
Rui Zhang, Haoyun Wang, Yao Xie
Abstract We consider online monitoring of the network event data to detect local changes in a cluster when the affected data stream distribution shifts from one point process to another with different parameters. Specifically, we are interested in detecting a change point that causes a shift of the underlying data distribution that follows a multivariate Hawkes process with exponential decay temporal kernel, whereby the Hawkes process is considered to account for spatiotemporal correlation between observations. The proposed detection procedure is based on scan score statistics. We derive the asymptotic distribution of the statistic, which enables the self-normalizing property and facilitates the approximation of the instantaneous false alarm probability and the average run length. When detecting a change in the Hawkes process with nonvanishing self-excitation, the procedure does not require estimating the postchange network parameter while assuming the temporal decay parameter, which enjoys computational efficiency. We further present an efficient procedure to accurately determine the false discovery rate via importance sampling, as validated by numerical examples. Using simulated and real stock exchange data, we show the effectiveness of the proposed method in detecting change while enjoying computational efficiency.
{"title":"Online score statistics for detecting clustered change in network point processes","authors":"Rui Zhang, Haoyun Wang, Yao Xie","doi":"10.1080/07474946.2022.2164307","DOIUrl":"https://doi.org/10.1080/07474946.2022.2164307","url":null,"abstract":"Abstract We consider online monitoring of the network event data to detect local changes in a cluster when the affected data stream distribution shifts from one point process to another with different parameters. Specifically, we are interested in detecting a change point that causes a shift of the underlying data distribution that follows a multivariate Hawkes process with exponential decay temporal kernel, whereby the Hawkes process is considered to account for spatiotemporal correlation between observations. The proposed detection procedure is based on scan score statistics. We derive the asymptotic distribution of the statistic, which enables the self-normalizing property and facilitates the approximation of the instantaneous false alarm probability and the average run length. When detecting a change in the Hawkes process with nonvanishing self-excitation, the procedure does not require estimating the postchange network parameter while assuming the temporal decay parameter, which enjoys computational efficiency. We further present an efficient procedure to accurately determine the false discovery rate via importance sampling, as validated by numerical examples. Using simulated and real stock exchange data, we show the effectiveness of the proposed method in detecting change while enjoying computational efficiency.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"42 1","pages":"70 - 89"},"PeriodicalIF":0.8,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45191763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-03DOI: 10.1080/07474946.2022.2070214
N. Mukhopadhyay, Swathi Venkatesan
Abstract Research on classical fixed-width confidence interval (FWCI) estimation problems for a normal mean when the variance remains unknown have steadily moved along under a zero-one loss function. On the other hand, minimum risk point estimation (MRPE) problems have grown largely under a squared error loss function plus sampling cost. However, the FWCI problems customarily do not take into account any sampling cost in their formulations. This fundamental difference between the two treatments has led the literature on the FWCI and MRPE problems to grow in multiple directions in their own separate ways from one another. In this article, we introduce a new formulation combining both MRPE and FWCI methodologies with desired asymptotic first-order (Theorems 2.1–2.2) and asymptotic second-order characteristics (Theorem 2.3) under a single unified structure, allowing us to develop a genuine minimum risk fixed-width confidence interval (MRFWCI) estimation strategy. Fruitful ideas are proposed by incorporating illustrations from purely sequential and other multistage MRFWCI problems with an explicit presence of a cost function incurred due to sampling. We supplement the general theory and methodology by means of illustrations and analyses from simulated data along with applications to air quality data.
{"title":"A new formulation of minimum risk fixed-width confidence interval (MRFWCI) estimation problems for a normal mean with illustrations and simulations: Applications to air quality data","authors":"N. Mukhopadhyay, Swathi Venkatesan","doi":"10.1080/07474946.2022.2070214","DOIUrl":"https://doi.org/10.1080/07474946.2022.2070214","url":null,"abstract":"Abstract Research on classical fixed-width confidence interval (FWCI) estimation problems for a normal mean when the variance remains unknown have steadily moved along under a zero-one loss function. On the other hand, minimum risk point estimation (MRPE) problems have grown largely under a squared error loss function plus sampling cost. However, the FWCI problems customarily do not take into account any sampling cost in their formulations. This fundamental difference between the two treatments has led the literature on the FWCI and MRPE problems to grow in multiple directions in their own separate ways from one another. In this article, we introduce a new formulation combining both MRPE and FWCI methodologies with desired asymptotic first-order (Theorems 2.1–2.2) and asymptotic second-order characteristics (Theorem 2.3) under a single unified structure, allowing us to develop a genuine minimum risk fixed-width confidence interval (MRFWCI) estimation strategy. Fruitful ideas are proposed by incorporating illustrations from purely sequential and other multistage MRFWCI problems with an explicit presence of a cost function incurred due to sampling. We supplement the general theory and methodology by means of illustrations and analyses from simulated data along with applications to air quality data.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"241 - 274"},"PeriodicalIF":0.8,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49286165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-03DOI: 10.1080/07474946.2022.2043054
Yanhong Wu, W. Wu
Abstract In this article, motivated by detecting the occurrence of an epidemic when the arrival rates of patients increase in a portion of M panels or detecting the deterioration of a system composed of M independent components that causes an increase in failure rates in a portion of components, we consider the detection of a common change when M independent Poisson processes are monitored simultaneously where only a portion of the processes have rate increases after the change time. M individual cumulative sum (CUSUM) processes and Shiryaev-Roberts (S-R) processes are calculated recursively in parallel at each pooled arrival time. A systematic procedure is proposed by using the sum of M S-R processes as the detection process for a common change. After the detection, the M individual CUSUM processes are used to isolate the changed panels with false discovery rate (FDR) control and then the medians of the change time estimates from each individual CUSUM process or S-R process based on the isolated panels are used to estimate the common change time. The model can be generalized to different prechange rates, jittered change time, and unknown postchange rates.
{"title":"Sequential common change detection, isolation, and estimation in multiple poisson processes","authors":"Yanhong Wu, W. Wu","doi":"10.1080/07474946.2022.2043054","DOIUrl":"https://doi.org/10.1080/07474946.2022.2043054","url":null,"abstract":"Abstract In this article, motivated by detecting the occurrence of an epidemic when the arrival rates of patients increase in a portion of M panels or detecting the deterioration of a system composed of M independent components that causes an increase in failure rates in a portion of components, we consider the detection of a common change when M independent Poisson processes are monitored simultaneously where only a portion of the processes have rate increases after the change time. M individual cumulative sum (CUSUM) processes and Shiryaev-Roberts (S-R) processes are calculated recursively in parallel at each pooled arrival time. A systematic procedure is proposed by using the sum of M S-R processes as the detection process for a common change. After the detection, the M individual CUSUM processes are used to isolate the changed panels with false discovery rate (FDR) control and then the medians of the change time estimates from each individual CUSUM process or S-R process based on the isolated panels are used to estimate the common change time. The model can be generalized to different prechange rates, jittered change time, and unknown postchange rates.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"176 - 197"},"PeriodicalIF":0.8,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43729064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-26DOI: 10.1080/07474946.2022.2109673
A. Novikov, Fahil Farkhshatov
Abstract In this article, we deal with problems of testing hypotheses in the framework of sequential statistical analysis. The main concern is the optimal design and performance evaluation of sampling plans in the Kiefer-Weiss problems. For the case of observations following a discrete exponential family, we provide algorithms for optimal design in the modified Kiefer-Weiss problem and obtain formulas for evaluating their performance, calculating operating characteristic function, average sample number, and some related characteristics. These formulas cover, as a particular case, sequential probability ratio tests (SPRTs) and their truncated versions, as well as optimal finite-horizon sequential tests. On the basis of the developed algorithms we propose a method of construction of optimal tests and their performance evaluation for the original Kiefer-Weiss problem. All algorithms are implemented as functions in the R programming language and can be downloaded from https://github.com/tosinabase/Kiefer-Weiss, where the code for the binomial, Poisson, and negative binomial distributions is readily available. Finally, we make numerical comparisons of the Kiefer-Weiss solution with the SPRT and the fixed-sample-size test having the same levels of the error probabilities.
{"title":"Design and performance evaluation in Kiefer-Weiss problems when sampling from discrete exponential families","authors":"A. Novikov, Fahil Farkhshatov","doi":"10.1080/07474946.2022.2109673","DOIUrl":"https://doi.org/10.1080/07474946.2022.2109673","url":null,"abstract":"Abstract In this article, we deal with problems of testing hypotheses in the framework of sequential statistical analysis. The main concern is the optimal design and performance evaluation of sampling plans in the Kiefer-Weiss problems. For the case of observations following a discrete exponential family, we provide algorithms for optimal design in the modified Kiefer-Weiss problem and obtain formulas for evaluating their performance, calculating operating characteristic function, average sample number, and some related characteristics. These formulas cover, as a particular case, sequential probability ratio tests (SPRTs) and their truncated versions, as well as optimal finite-horizon sequential tests. On the basis of the developed algorithms we propose a method of construction of optimal tests and their performance evaluation for the original Kiefer-Weiss problem. All algorithms are implemented as functions in the R programming language and can be downloaded from https://github.com/tosinabase/Kiefer-Weiss, where the code for the binomial, Poisson, and negative binomial distributions is readily available. Finally, we make numerical comparisons of the Kiefer-Weiss solution with the SPRT and the fixed-sample-size test having the same levels of the error probabilities.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"417 - 434"},"PeriodicalIF":0.8,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44850892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：