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Mathematics, Applied
Saman Hanif Shahbaz, Aisha Fayomi, Muhammad Qaiser Shahbaz
Summary: This paper proposes some estimators for the estimation of general population parameters. The estimators are for single-phase and two-phase sampling using information of single and multiple auxiliary variables. The bias and mean square errors of the proposed estimators are derived and compared with existing estimators for mean and variance. Specific cases of the proposed estimators are discussed. Simulation and numerical study are conducted to evaluate the performance of the proposed estimators.
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Mathematics, Applied
Sohaib Ahmad, Sardar Hussain, Muhammad Aamir, Faridoon Khan, Mohammed N. Alshahrani, Mohammed Alqawba
Summary: This paper proposes a new family of estimators for estimating the population mean for non-response using simple random sampling. The theoretical comparisons show that the proposed family of estimators is more efficient.
Article
Statistics & Probability
Zara Waseem, Hina Khan, Javid Shabbir
Summary: In this study, a generalized two-stage optional randomized response model was proposed using a single sample approach. A generalized exponential type estimator was developed for the mean of a sensitive variable, utilizing information from a non-sensitive auxiliary variable. Privacy protection and special cases of the suggested model were discussed, and simulation and numerical studies were conducted to assess the performance of the suggested estimator. The proposed model was compared with existing estimators, showing that it is more efficient under specific conditions.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2021)
Article
Mathematics, Applied
Housila P. Singh, Gajendra K. Vishwakarma, Harshada Joshi, Shubham Gupta
Summary: Shrinkage estimation is a fundamental tool in analyzing high-dimensional data. This study focuses on estimating the square of the location parameter in an exponential distribution when the coefficient of variation is known without error. Various estimators are proposed and compared, and the best unbiased estimator and the minimum mean square error estimator are identified. Numerical illustrations are provided to support the findings of this study.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2024)
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Computer Science, Interdisciplinary Applications
Iram Saleem, Aamir Sanaullah
Summary: The study introduced two new estimators for mean estimation of a sensitive variable in stratified sampling and proved their higher efficiency in certain situations compared to existing estimators.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2022)
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Computer Science, Software Engineering
Tolga Zaman, Murat Sagir, Mehmet Sahin
Summary: COVID-19 is a major issue globally, and understanding the risk levels of countries is crucial in determining appropriate measures. Risk assessment is more important than specific numbers like case counts and deaths.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Statistics & Probability
Aamir Sanaullah, Iram Saleem, Sat Gupta, Muhammad Hanif
Summary: This article proposes a generalized randomized response technique (RRT) model and develops exponential estimators in two-phase sampling using this model. The privacy protection level of the proposed RRT model is also discussed. Theoretical and empirical results are presented to examine the performance of the proposed mean estimators in comparison to other mean estimators.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
Summary: An exponential type estimator for the population mean of a sensitive study variable based on a non-sensitive auxiliary variable is proposed in this paper. Efficiency comparisons and simulation studies suggest that the proposed estimator outperforms existing estimators even with low correlation between auxiliary and study variables.
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
(2022)
Article
Mathematics, Applied
Sohaib Ahmad, Sardar Hussain, Javid Shabbir, Muhammad Aamir, M. El-Morshedy, Zubair Ahmad, Sharifah Alrajhi
Summary: This article addresses the problem of estimating the finite population mean using two auxiliary variables under a two-stage sampling scheme. It proposes an improved class of estimators in their generalized form and derives the mathematical properties of both existing and proposed estimators up to the first order of approximation. The study identifies 11 members of the improved generalized class of estimators that are more efficient in terms of the percentage relative efficiency. Two real data sets under a two-stage sampling are used to compare the performances of all considered estimators.
Article
Statistics & Probability
R. R. Sinha, Bharti
Summary: In this research article, improved regressed exponential estimators have been suggested for estimating the finite population mean utilizing the coefficient of skewness and kurtosis of an auxiliary character. The efficiency of the estimators is enhanced by incorporating other known parameters and constants. Theoretical and empirical discussions demonstrate the superior performance of the proposed estimators compared to traditional and recent relevant estimators.
JOURNAL OF STATISTICS AND MANAGEMENT SYSTEMS
(2022)
Article
Demography
Saurav Guha, Hukum Chandra
Summary: This study introduces improved chain-ratio estimators for population mean estimation based on two-phase sampling in the presence of non-response, comparing their biases and mean square errors with existing estimators. Empirical evaluations indicate that these estimators outperform other existing estimators in model-based and design-based simulations.
MATHEMATICAL POPULATION STUDIES
(2021)
Article
Social Sciences, Interdisciplinary
Joshua van Vuuren, Gary van Vuuren
Summary: Hedge funds play an important role in investment markets, but the increased occurrences of frauds and market manipulation require enhanced scrutiny. Market metrics that can quickly and reliably detect fraudulent activities are limited, but the bias ratio, along with other metrics, can offer a solution. High bias ratios, combined with statistical moments that significantly deviate from normal distributions, provide compelling evidence for possible return manipulation. Applied to known fraud cases, these metrics have the potential to serve as powerful early indicators of suspicious investment activity.
Article
Computer Science, Interdisciplinary Applications
Hyowon An, Kai Zhang, Hannu Oja, J. S. Marron
Summary: Identification of important variables in big data is a crucial challenge. To tackle this, methods for discovering variables with non-standard univariate marginal distributions are proposed. Traditional moments-based summary statistics can be sensitive to outliers, thus L-moments are considered for robustness. However, the limitation of L-moments is addressed by proposing Gaussian Centered L-moments.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Computer Science, Software Engineering
Nadia Mushtaq, Iram Saleem, Mustansar Aatizaz Amjad
Summary: This article explores the impact of COVID-19 on countries worldwide and focuses on evaluating the risks and lack of coping capacity in 190 countries. The study finds variations in socioeconomic vulnerability and coping capacity among different countries and proposes an exponential estimator for evaluation.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Demography
Tolga Zaman
Summary: Population stratification aims to increase the precision of estimation, using an efficient exponential ratio estimator to estimate the population mean in stratified random sampling can reduce bias and mean square error. The proposed estimators perform more efficiently under stratified random sampling, with lower mean square error compared to ratio and exponential estimators.
MATHEMATICAL POPULATION STUDIES
(2021)
Article
Mathematics, Applied
Lovleen Kumar Grover, Parmdeep Kaur
APPLIED MATHEMATICS AND COMPUTATION
(2011)
Article
Mathematics, Applied
Lovleen Kumar Grover, Parmdeep Kaur, Gajendra K. Vishawkarma
APPLIED MATHEMATICS AND COMPUTATION
(2012)
Correction
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2019)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2020)
Article
Mathematical & Computational Biology
Lovleen Kumar Grover, Amanpreet Kaur
BIOMETRICAL JOURNAL
(2020)
Article
Mathematics, Applied
Lovleen Kumar Grover, Amanpreet Kaur
Summary: This paper proposes an improved estimator for population mean in simple random sampling and stratified random sampling, as well as new robust estimators through traditional estimator improvement. The proposed traditional estimator is found to be more efficient than its competitors, and the robust estimator based on robust regression method is also more efficient under certain conditions. The theoretical results are verified through empirical examples and simulation studies.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2021)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
Summary: This paper proposes a generalized randomized response model for obtaining an unbiased estimator of sensitive population proportion, and calculates the variance of this estimator. The study shows that the proposed model is more efficient than various existing randomized response models under very easy attainable conditions.
JOURNAL OF STATISTICS AND MANAGEMENT SYSTEMS
(2022)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
Summary: An exponential type estimator for the population mean of a sensitive study variable based on a non-sensitive auxiliary variable is proposed in this paper. Efficiency comparisons and simulation studies suggest that the proposed estimator outperforms existing estimators even with low correlation between auxiliary and study variables.
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
(2022)
Article
Statistics & Probability
Lovleen Kumar Grover, Anchal Sharma
Summary: This paper addresses the problem of estimating the finite population mean using two auxiliary variables and a predictive approach in a two-phase sampling scheme, particularly in the presence of missing values and unknown population mean. Four classes of estimators are proposed based on this predictive approach. The expressions for bias and mean square errors are derived up to the first order of approximation. Optimal values of the involved constants are obtained to obtain the minimum mean square errors. The performance of the proposed estimators is evaluated through empirical and graphical comparisons with regression type estimators, both under single phase and double phase sampling schemes. Real and simulated data sets are used to validate the theoretical results, demonstrating the superiority of the proposed estimators in terms of percent relative efficiencies.
JOURNAL OF STATISTICAL THEORY AND APPLICATIONS
(2023)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
Summary: The article focuses on using ranks of original auxiliary variable as an additional auxiliary variable to propose an efficient population variance estimator. Bias and mean square error expressions of the proposed estimator, up to first order of approximation, are obtained. Efficiency comparisons with various existing estimators are conducted theoretically and numerically.
JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS
(2021)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
JOURNAL OF STATISTICAL THEORY AND PRACTICE
(2020)
Article
Mathematics
Lovleen Kumar Grover, Amanpreet Kaur
COMMUNICATIONS IN MATHEMATICS AND STATISTICS
(2019)
Article
Statistics & Probability
Lovleen Kumar Grover, Amanpreet Kaur
PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH
(2019)