Abstract Emerging sensor technology has spawned renewed interest in the deployment of space-time ... more Abstract Emerging sensor technology has spawned renewed interest in the deployment of space-time adaptive processing (STAP) based airborne early warning (AEW) radar. Numerous theoretical studies and simulation analyses have clearly identified the potential ...
This article contains our responses to expert' comments on our hybrid sequential procedure f... more This article contains our responses to expert' comments on our hybrid sequential procedure for selecting the best among several binomial populations in comparison with a control. The discussants raised a variety of both theoretical and practical issues contributing to our paper and opening directions for further research.
ABSTRACT A sequential selection procedure is proposed for comparing experimental Bernoulli popula... more ABSTRACT A sequential selection procedure is proposed for comparing experimental Bernoulli populations with a controlled Bernoulli population. The comparison is made according to the success probability. Based on the numbers of successes of the populations, the indifference zone formulation and the subset selection formulation are integrated to select either the best population or a random-sized subset that contains the best population. Observations are taken one at a time and the populations that are no longer comparable are eliminated until either the best is identified or the total number of observations in at least one population reaches a specific upper bound. We show that the proposed sequential procedure satisfies the same probability requirements as does the corresponding fixed-sample-size procedure. Furthermore, the expected sample size from each population for the proposed procedure is significantly smaller than the sample size required by the fixed-sample-size procedure.
A two-stage selection and testing design is proposed for choosing among k (≥ 2) experimental trea... more A two-stage selection and testing design is proposed for choosing among k (≥ 2) experimental treatments, provided it is better than a specific standard. In the first (selection) stage, ranking and selection formulation is adopted to select the one most promising treatment. In the second (testing) stage, hypothesis testing formulation is used to determine if the treatment selected at the first stage is better than the standard. The design allows for early termination at stage one if none of the treatments seem promising. The treatments are assumed to follow normal distributions with unknown means and unknown variances. “Better than the standard” means the population mean of an experimental treatment is larger than the standard. Appropriate definitions of size and power are given. Sample size requirements are compared with an analogous pure selection procedure of Taneja and Dudewicz (1992).
Abstract This article contains our responses to expert' comments on our hybrid sequential pro... more Abstract This article contains our responses to expert' comments on our hybrid sequential procedure for selecting the best among several binomial populations in comparison with a control. The discussants raised a variety of both theoretical and practical issues contributing to our paper and opening directions for further research.
Probability in the Engineering and Informational Sciences, 1987
Two procedures for the group-testing problem based on the Shannon-entropy criteria are proposed. ... more Two procedures for the group-testing problem based on the Shannon-entropy criteria are proposed. The model considered is that the N units are realizations of N Bernoulli independent and identically distributed (i.i.d.) chance variables with common, known probability q of an arbitrary unit being good and p =1 – q of it being defective. Both the algorithms introduced have low design complexity and yet provide near-optimal result. For N ≤ 5, one of the procedures introduced is optimal for selected values of q.
Journal of the American Society for Information Science and Technology, 2009
In the cited paper we used Bonferroni’s procedure to calculate the confidence intervals. However,... more In the cited paper we used Bonferroni’s procedure to calculate the confidence intervals. However, we made a mistake when applying the formula for calculating the confidence coefficients by confusing division in tα/2m with multiplication (tα∗2m) during the calculation process. That is, we inadvertently used α × 2m instead of α ÷ 2m as the tail probability. The correct formula for calculating the confidence intervals is as follows:
Journal of the American Society for Information Science and Technology, 2008
From a user-centered perspective, an effective search engine needs to attract new users to try ou... more From a user-centered perspective, an effective search engine needs to attract new users to try out its features, and retain those users so that they continue using the features. In this article, we...
Journal of Statistical Planning and Inference, 2009
We propose optimal procedures to achieve the goal of partitioning k multivariate normal populatio... more We propose optimal procedures to achieve the goal of partitioning k multivariate normal populations into two disjoint subsets with respect to a given standard vector. Definition of good or bad multivariate normal populations is given according to their Mahalanobis distances to a known standard vector as being small or large. Partitioning k multivariate normal populations is reduced to partitioning k non-central Chi-square or non-central F distributions with respect to the corresponding non-centrality parameters depending on whether the covariance matrices are known or unknown. The minimum required sample size for each population is determined to ensure that the probability of correct decision attains a certain level. An example is given to illustrate our procedures.
Abstract Emerging sensor technology has spawned renewed interest in the deployment of space-time ... more Abstract Emerging sensor technology has spawned renewed interest in the deployment of space-time adaptive processing (STAP) based airborne early warning (AEW) radar. Numerous theoretical studies and simulation analyses have clearly identified the potential ...
This article contains our responses to expert' comments on our hybrid sequential procedure f... more This article contains our responses to expert' comments on our hybrid sequential procedure for selecting the best among several binomial populations in comparison with a control. The discussants raised a variety of both theoretical and practical issues contributing to our paper and opening directions for further research.
ABSTRACT A sequential selection procedure is proposed for comparing experimental Bernoulli popula... more ABSTRACT A sequential selection procedure is proposed for comparing experimental Bernoulli populations with a controlled Bernoulli population. The comparison is made according to the success probability. Based on the numbers of successes of the populations, the indifference zone formulation and the subset selection formulation are integrated to select either the best population or a random-sized subset that contains the best population. Observations are taken one at a time and the populations that are no longer comparable are eliminated until either the best is identified or the total number of observations in at least one population reaches a specific upper bound. We show that the proposed sequential procedure satisfies the same probability requirements as does the corresponding fixed-sample-size procedure. Furthermore, the expected sample size from each population for the proposed procedure is significantly smaller than the sample size required by the fixed-sample-size procedure.
A two-stage selection and testing design is proposed for choosing among k (≥ 2) experimental trea... more A two-stage selection and testing design is proposed for choosing among k (≥ 2) experimental treatments, provided it is better than a specific standard. In the first (selection) stage, ranking and selection formulation is adopted to select the one most promising treatment. In the second (testing) stage, hypothesis testing formulation is used to determine if the treatment selected at the first stage is better than the standard. The design allows for early termination at stage one if none of the treatments seem promising. The treatments are assumed to follow normal distributions with unknown means and unknown variances. “Better than the standard” means the population mean of an experimental treatment is larger than the standard. Appropriate definitions of size and power are given. Sample size requirements are compared with an analogous pure selection procedure of Taneja and Dudewicz (1992).
Abstract This article contains our responses to expert' comments on our hybrid sequential pro... more Abstract This article contains our responses to expert' comments on our hybrid sequential procedure for selecting the best among several binomial populations in comparison with a control. The discussants raised a variety of both theoretical and practical issues contributing to our paper and opening directions for further research.
Probability in the Engineering and Informational Sciences, 1987
Two procedures for the group-testing problem based on the Shannon-entropy criteria are proposed. ... more Two procedures for the group-testing problem based on the Shannon-entropy criteria are proposed. The model considered is that the N units are realizations of N Bernoulli independent and identically distributed (i.i.d.) chance variables with common, known probability q of an arbitrary unit being good and p =1 – q of it being defective. Both the algorithms introduced have low design complexity and yet provide near-optimal result. For N ≤ 5, one of the procedures introduced is optimal for selected values of q.
Journal of the American Society for Information Science and Technology, 2009
In the cited paper we used Bonferroni’s procedure to calculate the confidence intervals. However,... more In the cited paper we used Bonferroni’s procedure to calculate the confidence intervals. However, we made a mistake when applying the formula for calculating the confidence coefficients by confusing division in tα/2m with multiplication (tα∗2m) during the calculation process. That is, we inadvertently used α × 2m instead of α ÷ 2m as the tail probability. The correct formula for calculating the confidence intervals is as follows:
Journal of the American Society for Information Science and Technology, 2008
From a user-centered perspective, an effective search engine needs to attract new users to try ou... more From a user-centered perspective, an effective search engine needs to attract new users to try out its features, and retain those users so that they continue using the features. In this article, we...
Journal of Statistical Planning and Inference, 2009
We propose optimal procedures to achieve the goal of partitioning k multivariate normal populatio... more We propose optimal procedures to achieve the goal of partitioning k multivariate normal populations into two disjoint subsets with respect to a given standard vector. Definition of good or bad multivariate normal populations is given according to their Mahalanobis distances to a known standard vector as being small or large. Partitioning k multivariate normal populations is reduced to partitioning k non-central Chi-square or non-central F distributions with respect to the corresponding non-centrality parameters depending on whether the covariance matrices are known or unknown. The minimum required sample size for each population is determined to ensure that the probability of correct decision attains a certain level. An example is given to illustrate our procedures.
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