Relevant bibliographies by topics / Unbiased ratio estimator

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Academic literature on the topic 'Unbiased ratio estimator'

Author: Grafiati
Published: 8 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Unbiased ratio estimator"

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Sohail, Muhammad Umair, Nursel Koyuncu, and Muhammad Areeb Iqbal Sethi. "Almost Unbiased Estimation of Coefficient of Dispersion from Incomplete Data." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 143–54. http://dx.doi.org/10.52700/scir.v3i2.110.

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This article develops an almost unbiased estimation of coefficient of dispersion by the productive use of coefficient of dispersion of the auxiliary variable in two phase sampling. Expressions for variances of the proposed estimators are obtained up to first order of approximation. The relative comparision of proposed unbiased ratio estimator are compared with navie estimator by using simulated data sets. Thus, we conclude that the suggested imputation methodology is more efficient than traditional estimator.
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Sohail, Muhammad Umair, Nursel Koyuncu, and Muhammad Areeb Iqbal Sethi. "Almost Unbiased Estimation of Coefficient of Dispression from Imputed Data." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 143–54. http://dx.doi.org/10.52700/scir.v3i2.55.

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This article develops an almost unbiased estimation of coefficient of dispersion by the productive use of coefficient of dispersion of the auxiliary variable in two phase sampling. Expressions for variances of the proposed estimators are obtained up to first order of approximation. The relative efficiencies of proposed unbiased ratio estimator are compared with navie estimator by using simulated data sets. Thus, we conclude that the proposed imputation procedure is more efficient than traditional estimator.
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Sohail, Muhammad Umair, Nursel Koyuncu, and Muhammad Areeb Iqbal Sethi. "Almost Unbiased Estimation of Coefficient of Dispression from Imputed Data." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 143–54. http://dx.doi.org/10.52700/scir.v3i2.55.

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This article develops an almost unbiased estimation of coefficient of dispersion by the productive use of coefficient of dispersion of the auxiliary variable in two phase sampling. Expressions for variances of the proposed estimators are obtained up to first order of approximation. The relative efficiencies of proposed unbiased ratio estimator are compared with navie estimator by using simulated data sets. Thus, we conclude that the proposed imputation procedure is more efficient than traditional estimator.
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Mehta, Nitu, and V. L. Mandowara. "Some Efficient Methods to Remove Bias in Ratio and Product Types Estimators in Ranked Set Sampling." International Journal of Bio-resource and Stress Management 13, no. 3 (2022): 276–82. http://dx.doi.org/10.23910/1.2022.2771a.

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Ranked set sampling is one method to potentially increase precision and reduce costs by using quantitative or qualitative information to obtain a more representative sample. Use of auxiliary information has shown its significance in improvement of efficiency of estimators of unknown population parameters. Ratio estimator is used when auxiliary information in the form of population mean of auxiliary variable at estimation stage for the estimation of population parameters when study and auxiliary variable are positively correlated. In case of negative correlation between study variable and auxil
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Yadav, Rohini, Lakshmi N. Upadhyaya, Housila P. Singh, and S. Chatterjee. "Almost unbiased ratio and product type exponential estimators." Statistics in Transition new series 13, no. 3 (2013): 537–50. http://dx.doi.org/10.59170/stattrans-2012-038.

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This paper considers the problem of estimating the population mean Y of the study variate y using information on auxiliary variate x. We have suggested a generalized version of Bahl and Tuteja (1991) estimator and its properties are studied. It is found that asymptotic optimum estimator (AOE) in the proposed generalized version of Bahl and Tuteja (1991) estimator is biased. In some applications, biasedness of an estimator is disadvantageous. So applying the procedure of Singh and Singh (1993) we derived an almost unbiased version of AOE. A numerical illustration is given in the support of the
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Sharma, Rubal, and Sangeeta Malik. "Exponential Type Ratio and Product Estimator of Finite Population Mean in Stratified Sampling." Indian Journal Of Science And Technology 17, no. 40 (2024): 4168–76. http://dx.doi.org/10.17485/ijst/v17i40.2237.

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Objective: This article presents an exponential-type ratio and product estimator of the finite population mean within the stratification framework. A common technique in survey sampling is stratification, which divides the population into similar subsets to improve the estimator's accuracy. Methods: This paper tackles the problem of determining the expression for bias and Mean Square Error (MSE) for the population up to the 1st degree of the approximation when stratification is present. Findings: Here it is obtained the optimum value of the modified estimator. The modified estimator is more ef
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Xu, Jianwen, and Hu Yang. "Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors." Acta et Commentationes Universitatis Tartuensis de Mathematica 15, no. 1 (2020): 27–43. http://dx.doi.org/10.12697/acutm.2011.15.03.

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In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the proposed estimators are derived and compared. Sufficient conditions on the departure parameter ∆ and the ridge parameter k are derived for the proposed estimators to be superior to the almost unbiased r
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Jambulingam, Subramani, and Ajith S. Master. "Almost Unbiased Ratio cum Product Estimator for Finite Population Mean with Known Median in Simple Random Sampling." Nepalese Journal of Statistics 1 (December 29, 2017): 1–14. http://dx.doi.org/10.3126/njs.v1i0.18813.

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Introduction: In sampling theory, different procedures are used to obtain the efficient estimator of the population mean. The commonly used method is to obtain the estimator of the population mean is simple random sampling without replacement when there is no auxiliary variable is available. There are methods that use auxiliary information of the study characteristics. If the auxiliary variable is correlated with study variable, number of estimators are widely available in the literature.Objective: This study deals with a new ratio cum product estimator is developed for the estimation of popul
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Singh, Rajesh, Mukesh Kumar, Pankaj Chauhan, Nirmala Sawan, and Florentin Smarandache. "A general family of dual to ratio-cum-product estimator in sample surveys." Statistics in Transition new series 12, no. 3 (2011): 587–94. http://dx.doi.org/10.59170/stattrans-2011-044.

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This paper presents a family of dual to ratio-cum-product estimators for the finite population mean. Under simple random sampling without replacement (SRSWOR) scheme, expressions of the bias and mean-squared error (MSE) up to the first order of approximation are derived. We show that the proposed family is more efficient than usual unbiased estimator, ratio estimator, product estimator, Singh estimator (1967), Srivenkataramana (1980) and Bandyopadhyaya estimator (1980) and Singh et al. (2005) estimator. An empirical study is carried out to illustrate the performance of the constructed estimato
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Mittal, Alisha, and Manoj Kumar. "GENERALISED EXPONENTIAL RATIO-TYPE ESTIMATOR FOR FINITE POPULATION VARIANCE UNDER RANDOM NON-RESPONSE." International Journal of Advanced Research 9, no. 01 (2021): 589–96. http://dx.doi.org/10.21474/ijar01/12332.

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In this research paper an effort has been made for the estimation of population variance of the study variable by using information on certain known parameters of the auxiliary variable under non-response for scheme I and II given by Singh and Joarder (1998). Generalized exponential ratio-type estimator has been proposed and their properties have been studied under non response techniques and conditions were found when the family of proposed estimators identified by using different choices for (P, Q) performed better than the usual unbiased estimator. It was also observed that for different va
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Book chapters on the topic "Unbiased ratio estimator"

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Groß, Thomas. "Statistical Reliability of 10 Years of Cyber Security User Studies." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79318-0_10.

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AbstractBackground. In recent years, cyber security user studies have been appraised in meta-research, mostly focusing on the completeness of their statistical inferences and the fidelity of their statistical reporting. However, estimates of the field’s distribution of statistical power and its publication bias have not received much attention.Aim. In this study, we aim to estimate the effect sizes and their standard errors present as well as the implications on statistical power and publication bias.Method. We built upon a published systematic literature review of 146 user studies in cyber se
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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Ratio and regression estimation." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0007.

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Inexpensive and/or readily available auxiliary variable, x, values may often be available at little or no cost. If these variables are highly correlated with the target variable, y, then use of ratio or regression estimators may greatly reduce sampling variance. These estimators are not unbiased, but bias is generally small compared to the target of estimation and contributes a very small proportion of overall mean square error, the relevant measure of accuracy for biased estimators. Ratio estimation can also be incorporated in the context of stratified designs, again possibly offering a reduc
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Raftery, Adrian E., Michael A. Newton, Jaya M. Satagopan, and Pavel N. Krivitsky. "Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity." In Bayesian Statistics 8. Oxford University PressOxford, 2007. http://dx.doi.org/10.1093/oso/9780199214655.003.0015.

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Abstract The integrated likelihood (also called the marginal likelihood or the normalizing constant) is a central quantity in Bayesian model selection and model averaging. It is defined as the integral over the parameter space of the likelihood times the prior density. The Bayes factor for model comparison and Bayesian testing is a ratio of integrated likelihoods, and the model weights in Bayesian model averaging are proportional to the integrated likelihoods. We consider the estimation of the integrated likelihood from posterior simulation output, aiming at a generic method that uses only the
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Conference papers on the topic "Unbiased ratio estimator"

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Tsao, Sheng-Kai, and Jenho Tsao. "An Unbiased Tissue Attenuation Coefficient Estimator Using Microbubble Echoes with Harmonic Ratio Compensation." In 2012 International Conference on Biomedical Engineering and Biotechnology (iCBEB). IEEE, 2012. http://dx.doi.org/10.1109/icbeb.2012.64.

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Winick, Kim A. "Cramer-Rao lower bounds on the performance of CCD optical position estimators." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.fi2.

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The problem of optically estimating an object’s position using a charge coupled device (CCD) array composed of square pixels Δx on a side is analyzed. The object’s image spot at the CCD is assumed to have a Gaussian intensity profile with a 1/e point at a radial distance of 2 σ s from the peak, and the CCD noise is modeled as Poisson distributed dark current shot noise. A 2-D Cramer-Rao bound is developed and used to determine a lower limit for the mean-square error of any unbiased position estimator, and the maximum likelihood estimator is also derived. For the 1-D position estimation problem
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Schwarz, Andreas, and Walter Kellermann. "Unbiased coherent-to-diffuse ratio estimation for dereverberation." In 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2014. http://dx.doi.org/10.1109/iwaenc.2014.6953306.

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Ramkumar, Barathram, Marco P. Schoen, and Feng Lin. "Application of an Intelligent Hybrid Optimization Technique for Parameter Estimation in the Presence of Colored Noise." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41352.

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Parameter estimation is an important concept in engineering where a mathematical model of a system is identified with the help of input and output signals. The Classical Least Squares (LS) algorithm gives an unbiased estimate of the parameters when the system noise is white. This property is lost when the system noise is colored — which is generally the case. In order to overcome the bias problem associated with the colored noise environment, one can use a whitening filter. The cost function in the case of a colored noise environment becomes multimodal when the signal to noise ratio is high an
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Reports on the topic "Unbiased ratio estimator"

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Zheng, Ying. Evaluating the Assumptions Required for the Prevalence Odds Ratio to Unbiasedly Estimate the Incidence Density Rate Ratio. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-645.

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