Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72016
M. K. Panda
In comparison to Scheffè’s canonical polynomial models (S-models), the Kronecker models (K-models) for mixture experiments are symmetric, compact in notation, and based on the Kronecker algebra of vectors and matrices. Further, there is a corresponding transition from S-models to K-models in the form of model re-parameterization. In the literature, it has been recommended to use second-degree K-models in practice compared to the widely used second-degree S-models especially when the moment matrix is of an ill-conditioning type. The motivation of the present article is to discriminate between K-models and S-models in terms of the model-robust D- and A-optimality criteria. These optimality criteria are discussed when there is uncertainty in selecting an appropriate model out of two rival models for a mixture experiment. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 31-48
{"title":"Model Robust Optimal Designs for Kronecker Model for Mixture Experiments","authors":"M. K. Panda","doi":"10.3329/ijss.v24i1.72016","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72016","url":null,"abstract":"In comparison to Scheffè’s canonical polynomial models (S-models), the Kronecker models (K-models) for mixture experiments are symmetric, compact in notation, and based on the Kronecker algebra of vectors and matrices. Further, there is a corresponding transition from S-models to K-models in the form of model re-parameterization. In the literature, it has been recommended to use second-degree K-models in practice compared to the widely used second-degree S-models especially when the moment matrix is of an ill-conditioning type. The motivation of the present article is to discriminate between K-models and S-models in terms of the model-robust D- and A-optimality criteria. These optimality criteria are discussed when there is uncertainty in selecting an appropriate model out of two rival models for a mixture experiment.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 31-48","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72021
Arijit Chaudhuri, Dipika Patra
Hartley and Ross’s (1954) ratio-type unbiased estimator for a finite population total based on a Simple Random Sample taken Without Replacement (SRSWOR) is examined for its performance versus the expansion estimator from the sample data at hand by Chaudhuri and Samaddar (2022). They also examined how Des Raj (1956) estimator based on PPSWOR performs against SRSWOR combined with expansion estimator using PPSWOR sample values. Here we study the expansion of them to Randomized Response survey data. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 75-84
{"title":"Estimating Gain in Efficiency in Complicated Randomized Response Surveys versus Simpler Alternatives","authors":"Arijit Chaudhuri, Dipika Patra","doi":"10.3329/ijss.v24i1.72021","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72021","url":null,"abstract":"Hartley and Ross’s (1954) ratio-type unbiased estimator for a finite population total based on a Simple Random Sample taken Without Replacement (SRSWOR) is examined for its performance versus the expansion estimator from the sample data at hand by Chaudhuri and Samaddar (2022). They also examined how Des Raj (1956) estimator based on PPSWOR performs against SRSWOR combined with expansion estimator using PPSWOR sample values. Here we study the expansion of them to Randomized Response survey data.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 75-84","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study variables are usually correlated with the auxiliary variables and therefore their correlation could easily be computed. In this paper, the correlation coefficient is used for the estimation procedure, and therefore, we proposed a Horvitz-Thompson ratio-type estimator using correlation coefficient for Balanced Sampling plan excluding Adjacent units (BSA plan). It has been illustrated theoretically and empirically that the proposed Horvitz-Thompson ratio-type estimator is more precise than the Horvitz-Thompson estimator based on BSA plan. The proposed estimator provides an opportunity to utilise the auxiliary information for the estimation of population mean for BSA plan and useful for many real-life experiments. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 103-114
{"title":"Ratio Type Estimator for Balanced Sampling Plan excluding Adjacent Units","authors":"Neeraj Tiwari, Jharna Banerjie, Girish Chandra, Shailja Bhari","doi":"10.3329/ijss.v24i1.72028","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72028","url":null,"abstract":"The study variables are usually correlated with the auxiliary variables and therefore their correlation could easily be computed. In this paper, the correlation coefficient is used for the estimation procedure, and therefore, we proposed a Horvitz-Thompson ratio-type estimator using correlation coefficient for Balanced Sampling plan excluding Adjacent units (BSA plan). It has been illustrated theoretically and empirically that the proposed Horvitz-Thompson ratio-type estimator is more precise than the Horvitz-Thompson estimator based on BSA plan. The proposed estimator provides an opportunity to utilise the auxiliary information for the estimation of population mean for BSA plan and useful for many real-life experiments.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 103-114","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72030
K. Dihidar
In general, the well known Chebyshev’s inequality is used to determine the sample size in order to conduct a survey using direct responses. The same technique intending to cover for sensitive variables are attempted recently by many statisticians. However it has been observed that in many cases the acceptable sample sizes are hard to be obtained, mainly because of appearance of some easily non-controllable part. Chaudhuri and Sen (2020), Chaudhuri and Patra (2023) and others have illustrated different situations and solutions are proposed therein. In this paper, following Chaudhuri, Bose and Dihidar (2011), we have made an attempt to deterimine the sample size corresponding to the estimators of sensitive population proportion using multiple randomized responses from distinct persons sampled. Along with the theoretical derivations, some numerical illustrations are presented. Based on the important extractions of our numerical illustration results, the recommendable sample size in practical real survey situations are observed. International Journal of Statistical Sciences, Vol. 24(1) March, 2024, pp 115-132
{"title":"On the Sample Size Determination based on the Randomized Response Surveys","authors":"K. Dihidar","doi":"10.3329/ijss.v24i1.72030","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72030","url":null,"abstract":"In general, the well known Chebyshev’s inequality is used to determine the sample size in order to conduct a survey using direct responses. The same technique intending to cover for sensitive variables are attempted recently by many statisticians. However it has been observed that in many cases the acceptable sample sizes are hard to be obtained, mainly because of appearance of some easily non-controllable part. Chaudhuri and Sen (2020), Chaudhuri and Patra (2023) and others have illustrated different situations and solutions are proposed therein. In this paper, following Chaudhuri, Bose and Dihidar (2011), we have made an attempt to deterimine the sample size corresponding to the estimators of sensitive population proportion using multiple randomized responses from distinct persons sampled. Along with the theoretical derivations, some numerical illustrations are presented. Based on the important extractions of our numerical illustration results, the recommendable sample size in practical real survey situations are observed.\u0000International Journal of Statistical Sciences, Vol. 24(1) March, 2024, pp 115-132","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72035
Purnima Shaw, Sanghamitra Pal
Randomized Response (RR) Technique (RRT) pioneered by Warner (1965) is a useful tool to elicit responses on sensitive characteristics, such as induced abortions, drug abuse, drunken driving, total amount of counterfeit notes of a particular denomination held by individuals in the population, etc. There exists a huge literature on Randomized Response (RR) devices for estimation of finite population mean of quantitative variables, sensitive in nature mostly based on Eichhorn and Hayre (1983). Device-I and Device-II vide Chaudhuri and Christofides (2013) allow estimation of population mean of sensitive quantitative variables using sample chosen by a general sampling design. On the other hand, Item Count Technique (ICT), described elaborately in Chaudhuri and Christofides (2013), is an alternative to RRT for respondents who do not choose to provide RRs. While some respondents may find a variable as sensitive, others may find it innocuous enough to provide a direct response (DR) about his/her true value. In such a case, Optional Randomized Response (ORR) Technique (ORRT) with options for DR and RR was introduced by Chaudhuri and Mukherjee (1985). Pal (2007) proposed an ORR device which offers choices for RR and ICT to the respondents for giving their answers. A new ORRT with options for DR, RR and ICT was provided by Shaw and Pal (2021) for eliciting indirect responses on sensitive characteristics. As this device relates to estimation of population proportion of sensitive characteristics, an attempt has been made to extend it for sensitive quantitative variables. Further, to take care of individuals’ varying choices for DR, RR and ICT and to protect the privacy of the respondents’ choices, this paper develops an ORR device allowing the respondents chosen by a general sampling design, to choose any one of the three options according to their choices. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 155-170
{"title":"Indirect Questioning Technique Related to Sensitive Quantitative Variables with Options for Direct, Randomized and Item Count Responses","authors":"Purnima Shaw, Sanghamitra Pal","doi":"10.3329/ijss.v24i1.72035","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72035","url":null,"abstract":"Randomized Response (RR) Technique (RRT) pioneered by Warner (1965) is a useful tool to elicit responses on sensitive characteristics, such as induced abortions, drug abuse, drunken driving, total amount of counterfeit notes of a particular denomination held by individuals in the population, etc. There exists a huge literature on Randomized Response (RR) devices for estimation of finite population mean of quantitative variables, sensitive in nature mostly based on Eichhorn and Hayre (1983). Device-I and Device-II vide Chaudhuri and Christofides (2013) allow estimation of population mean of sensitive quantitative variables using sample chosen by a general sampling design. On the other hand, Item Count Technique (ICT), described elaborately in Chaudhuri and Christofides (2013), is an alternative to RRT for respondents who do not choose to provide RRs. While some respondents may find a variable as sensitive, others may find it innocuous enough to provide a direct response (DR) about his/her true value. In such a case, Optional Randomized Response (ORR) Technique (ORRT) with options for DR and RR was introduced by Chaudhuri and Mukherjee (1985). Pal (2007) proposed an ORR device which offers choices for RR and ICT to the respondents for giving their answers. A new ORRT with options for DR, RR and ICT was provided by Shaw and Pal (2021) for eliciting indirect responses on sensitive characteristics. As this device relates to estimation of population proportion of sensitive characteristics, an attempt has been made to extend it for sensitive quantitative variables. Further, to take care of individuals’ varying choices for DR, RR and ICT and to protect the privacy of the respondents’ choices, this paper develops an ORR device allowing the respondents chosen by a general sampling design, to choose any one of the three options according to their choices. \u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 155-170","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72017
Syed Shahadat Hossain, Md Rafiqul Islam
This article details the development and implementation of a strategic sampling methodology aimed at enhancing disaster-related statistics in Bangladesh. The study focuses on creating a specialized sampling frame by conducting a comprehensive census of enumeration areas (mouzas) affected by natural disasters. Employing a two-stage random sampling technique, the methodology incorporates stratification at district and disaster-type levels to capture diverse disaster occurrences. The Kish allocation method is utilized for sample allocation, addressing disparities in district sizes. Through meticulous trial and error simulations, the study ensures minimum sample sizes within each domain while employing inverse probability weights to estimate parameters. This strategic approach adopts robust estimations, enriching insights into disaster-related statistics. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 49-64
{"title":"Crafting Disaster-Driven Statistics: A Strategic Sampling Model","authors":"Syed Shahadat Hossain, Md Rafiqul Islam","doi":"10.3329/ijss.v24i1.72017","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72017","url":null,"abstract":"This article details the development and implementation of a strategic sampling methodology aimed at enhancing disaster-related statistics in Bangladesh. The study focuses on creating a specialized sampling frame by conducting a comprehensive census of enumeration areas (mouzas) affected by natural disasters. Employing a two-stage random sampling technique, the methodology incorporates stratification at district and disaster-type levels to capture diverse disaster occurrences. The Kish allocation method is utilized for sample allocation, addressing disparities in district sizes. Through meticulous trial and error simulations, the study ensures minimum sample sizes within each domain while employing inverse probability weights to estimate parameters. This strategic approach adopts robust estimations, enriching insights into disaster-related statistics.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 49-64","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72032
Y. M. Singh, G. S. Sharma, Opendra Salam, B. K. Sinha
Chao's (1982) sampling scheme offers a systematic approach to select samples based on probability proportional to size (PPS) sampling without replacement but it might be difficult to grasp, particularly for entry-level researchers. In response, this study revisits Chao's method with the aim of providing a simplified and more intuitive understanding. Drawing inspiration from efforts by Dr. Tommy Wright and subsequent group discussions with BKS, we present a step-by-step breakdown of Chao's scheme with illustrative examples, emphasizing its elegance and practicality. This study showcases the value of making complex statistical methods accessible for broader engagement in research and practice. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 133-154
{"title":"Understanding Chao (Biometrika, 1982) [Paper on ΠPS Sampling Schemes]","authors":"Y. M. Singh, G. S. Sharma, Opendra Salam, B. K. Sinha","doi":"10.3329/ijss.v24i1.72032","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72032","url":null,"abstract":"Chao's (1982) sampling scheme offers a systematic approach to select samples based on probability proportional to size (PPS) sampling without replacement but it might be difficult to grasp, particularly for entry-level researchers. In response, this study revisits Chao's method with the aim of providing a simplified and more intuitive understanding. Drawing inspiration from efforts by Dr. Tommy Wright and subsequent group discussions with BKS, we present a step-by-step breakdown of Chao's scheme with illustrative examples, emphasizing its elegance and practicality. This study showcases the value of making complex statistical methods accessible for broader engagement in research and practice.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 133-154","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.71866
Manisha Pal
In a mixture experiment, the measured response is assumed to depend only on the relative proportions of ingredients or components present in the mixture. Scheffe´ (1958) first systematically considered this problem, and introduced different models and suitable designs. Optimum designs for the estimation of parameters in various mixture models are available in the literature. However, in a mixture experiment, interest is likely to be more on the optimum mixing proportions of the ingredients being used. In this exposition, we take the readers on a journey through the optimum designs developed for estimating the optimum mixture combination as accurately as possible under various mixture models. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 1-14
{"title":"Optimum Designs for Optimum Mixtures: An Informative Review","authors":"Manisha Pal","doi":"10.3329/ijss.v24i1.71866","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.71866","url":null,"abstract":"In a mixture experiment, the measured response is assumed to depend only on the relative proportions of ingredients or components present in the mixture. Scheffe´ (1958) first systematically considered this problem, and introduced different models and suitable designs. Optimum designs for the estimation of parameters in various mixture models are available in the literature. However, in a mixture experiment, interest is likely to be more on the optimum mixing proportions of the ingredients being used. In this exposition, we take the readers on a journey through the optimum designs developed for estimating the optimum mixture combination as accurately as possible under various mixture models.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 1-14","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72023
T. J. Rao
Prof. CR Rao has been awarded the prestigious 2023 International Prize in Statistics. The citation reads: “In his remarkable 1945 paper published in the Bulletin of the Calcutta Mathematical Society, Calyampudi Radhakrishna (C.R.) Rao demonstrated three fundamental results that paved the way for the modern field of Statistics and provided statistical tools heavily used in science today……”. These three results are ‘Cramer-Rao Lower Bound’ (CRLB), ‘Rao- Blackwellization’ (RB) and the third one now flourished as ‘Information Geometry’. In this paper, we shall discuss two offshoots from his work over the eight decades. Several articles have appeared on his life and work (see for example, T. J. Rao (2019 and 2023a, 2023b) and Kumar (2023)). The first offshoot is based on one of the three breakthrough results, namely, Rao–Blackwell Theorem, first proved by C.R. Rao in 1945, when he was just 25 years old and also by Blackwell later in 1947. The second one is on Association Rule Mining (ARM), which he developed when he was 96 years old. These two papers reveal the transition of statistical methodologies from Fisherian concepts to recent applications of AI and ML. In this paper we shall pose some questions which need further study. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 85-89
{"title":"Some Questions Related to Rao-Blackwellization and Association Rule Mining","authors":"T. J. Rao","doi":"10.3329/ijss.v24i1.72023","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72023","url":null,"abstract":"Prof. CR Rao has been awarded the prestigious 2023 International Prize in Statistics. The citation reads: “In his remarkable 1945 paper published in the Bulletin of the Calcutta Mathematical Society, Calyampudi Radhakrishna (C.R.) Rao demonstrated three fundamental results that paved the way for the modern field of Statistics and provided statistical tools heavily used in science today……”. These three results are ‘Cramer-Rao Lower Bound’ (CRLB), ‘Rao- Blackwellization’ (RB) and the third one now flourished as ‘Information Geometry’. In this paper, we shall discuss two offshoots from his work over the eight decades. Several articles have appeared on his life and work (see for example, T. J. Rao (2019 and 2023a, 2023b) and Kumar (2023)). The first offshoot is based on one of the three breakthrough results, namely, Rao–Blackwell Theorem, first proved by C.R. Rao in 1945, when he was just 25 years old and also by Blackwell later in 1947. The second one is on Association Rule Mining (ARM), which he developed when he was 96 years old. These two papers reveal the transition of statistical methodologies from Fisherian concepts to recent applications of AI and ML. In this paper we shall pose some questions which need further study.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 85-89","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.71870
Anurup Majumder, Hiranmoy Das, Ankita Dutta, D. Nishad
In the present study, an effort has been made to construct D-efficient covariate designs in BIB design (v, b, r, k and λ) set-up when either one of k and r is odd or both k and r are odd numbers and Hadamard matrix of order k i.e., Hk does not exist. For all the developed D-efficient designs, the covariates are mutually orthogonal to each other. The methods of construction of D-efficient covariate designs are developed with the help of a new matrix viz., Special Array (Das et. al., 2020). In this article, the series of developed D-efficient covariate designs are not available in the existing literature. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 15-30
在本研究中，我们努力在 BIB 设计（v, b, r, k 和 λ）中构建 D 效率协变量设计，即当 k 和 r 中的一个为奇数或 k 和 r 均为奇数，且 k 阶哈达玛矩阵（即 Hk）不存在时的设计。对于所有已开发的 D-效率设计，协变量之间都是相互正交的。D 效率协变量设计的构建方法是在新矩阵（即特殊阵列）的帮助下开发出来的（Das 等人，2020）。在本文中，所开发的一系列 D 效率协变量设计在现有文献中并不存在。
{"title":"New Series of D-efficient Covariate Designs under BIBD set-up","authors":"Anurup Majumder, Hiranmoy Das, Ankita Dutta, D. Nishad","doi":"10.3329/ijss.v24i1.71870","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.71870","url":null,"abstract":"In the present study, an effort has been made to construct D-efficient covariate designs in BIB design (v, b, r, k and λ) set-up when either one of k and r is odd or both k and r are odd numbers and Hadamard matrix of order k i.e., Hk does not exist. For all the developed D-efficient designs, the covariates are mutually orthogonal to each other. The methods of construction of D-efficient covariate designs are developed with the help of a new matrix viz., Special Array (Das et. al., 2020). In this article, the series of developed D-efficient covariate designs are not available in the existing literature.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 15-30","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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