Incorporating past data in enhancing coefficient of variation estimation: A practical approach

Poonam Singh; Prayas Sharma

Makale detayları

Incorporating past data in enhancing coefficient of variation estimation: A practical approach

Yazarlar:

Poonam Singh | Prayas Sharma

Yayıncı:

Hacettepe University

Ana Başlıklar:

Theory of Sampling

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1637-1656

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356974

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 Measurement of variability is a crucial issue in survey sampling. This problem takes on a practical perspective when considering relative variability, as real-life scenarios often involve comparing two different characteristics or the same characteristic on different scales. For researchers and professionals working in a variety of fields, including agriculture, the stock market, economics, public health, finance, and more, its ability to improve decision-making processes makes it an essential tool. In this paper, we employ the exponentially weighted moving average statistic to propose memory-type estimators for the coefficient of variation that take advantage of auxiliary information in time-scaled surveys. These estimators are based on the concept of memory-type statistics, which integrate both past and present data to enhance the estimation of population parameters. Designed to dynamically incorporate historical data, they improve the accuracy and efficiency of estimation in longitudinal surveys. To validate their effectiveness, we establish the necessary mathematical conditions and explicitly derive expressions for bias and mean square error. Observations from a simulation study indicate that incorporating historical sample data alongside current data significantly enhances estimator performance. In addition, two real-life case studies are presented to demonstrate the efficacy and superiority of the proposed estimators.

 Measurement of variability is a crucial issue in survey sampling. This problem takes on a practical perspective when considering relative variability, as real-life scenarios often involve comparing two different characteristics or the same characteristic on different scales. For researchers and professionals working in a variety of fields, including agriculture, the stock market, economics, public health, finance, and more, its ability to improve decision-making processes makes it an essential tool. In this paper, we employ the exponentially weighted moving average statistic to propose memory-type estimators for the coefficient of variation that take advantage of auxiliary information in time-scaled surveys. These estimators are based on the concept of memory-type statistics, which integrate both past and present data to enhance the estimation of population parameters. Designed to dynamically incorporate historical data, they improve the accuracy and efficiency of estimation in longitudinal surveys. To validate their effectiveness, we establish the necessary mathematical conditions and explicitly derive expressions for bias and mean square error. Observations from a simulation study indicate that incorporating historical sample data alongside current data significantly enhances estimator performance. In addition, two real-life case studies are presented to demonstrate the efficacy and superiority of the proposed estimators.

 Measurement of variability is a crucial issue in survey sampling. This problem takes on a practical perspective when considering relative variability, as real-life scenarios often involve comparing two different characteristics or the same characteristic on different scales. For researchers and professionals working in a variety of fields, including agriculture, the stock market, economics, public health, finance, and more, its ability to improve decision-making processes makes it an essential tool. In this paper, we employ the exponentially weighted moving average statistic to propose memory-type estimators for the coefficient of variation that take advantage of auxiliary information in time-scaled surveys. These estimators are based on the concept of memory-type statistics, which integrate both past and present data to enhance the estimation of population parameters. Designed to dynamically incorporate historical data, they improve the accuracy and efficiency of estimation in longitudinal surveys. To validate their effectiveness, we establish the necessary mathematical conditions and explicitly derive expressions for bias and mean square error. Observations from a simulation study indicate that incorporating historical sample data alongside current data significantly enhances estimator performance. In addition, two real-life case studies are presented to demonstrate the efficacy and superiority of the proposed estimators.

 Measurement of variability is a crucial issue in survey sampling. This problem takes on a practical perspective when considering relative variability, as real-life scenarios often involve comparing two different characteristics or the same characteristic on different scales. For researchers and professionals working in a variety of fields, including agriculture, the stock market, economics, public health, finance, and more, its ability to improve decision-making processes makes it an essential tool. In this paper, we employ the exponentially weighted moving average statistic to propose memory-type estimators for the coefficient of variation that take advantage of auxiliary information in time-scaled surveys. These estimators are based on the concept of memory-type statistics, which integrate both past and present data to enhance the estimation of population parameters. Designed to dynamically incorporate historical data, they improve the accuracy and efficiency of estimation in longitudinal surveys. To validate their effectiveness, we establish the necessary mathematical conditions and explicitly derive expressions for bias and mean square error. Observations from a simulation study indicate that incorporating historical sample data alongside current data significantly enhances estimator performance. In addition, two real-life case studies are presented to demonstrate the efficacy and superiority of the proposed estimators.

Makale detayları

Incorporating past data in enhancing coefficient of variation estimation: A practical approach

Yazarlar:

Poonam Singh | Prayas Sharma

Yayıncı:

Hacettepe University

Ana Başlıklar:

Theory of Sampling

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1637-1656

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356974

pISSN:

2651-477X

Bias

coefficient of variation

exponentially weighted moving average statistic

mean square error

memory type estimators

simulation

Sayı

Makale detayları

Incorporating past data in enhancing coefficient of variation estimation: A practical approach

Yazarlar: Poonam Singh | Prayas Sharma

Yayıncı: Hacettepe University

Ana Başlıklar: Theory of Sampling

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1637-1656

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356974

pISSN: 2651-477X

Bias

coefficient of variation

exponentially weighted moving average statistic

mean square error

memory type estimators

simulation

Sayı

Yazarlar:

Poonam Singh | Prayas Sharma

Yayıncı:

Hacettepe University

Ana Başlıklar:

Theory of Sampling

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1637-1656

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356974

pISSN:

2651-477X