Relevant bibliographies by topics / Unbiased ratio estimator

Academic literature on the topic 'Unbiased ratio estimator'

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Published: 8 September 2023

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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 (December 31, 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 (December 31, 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 (December 31, 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 (March 31, 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 auxiliary variable, Product estimator is defined for the estimation of population mean. This paper proposed the problem of reducing the bias of the ratio and product estimators of the population mean in ranked set sampling (RSS). This paper suggested several type unbiased estimators of the finite population mean using information on known population parameters of the auxiliary variable in ranked set sampling. An important objective in any statistical estimation procedure is to obtain the estimators of parameters of interest with more precision. The Variance of the proposed unbiased ratio and product estimators are obtained up to first degree of approximation. Theoretically, it is shown that these suggested estimators are more efficient than the unbiased estimators in Simple random sampling. A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.
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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 population mean of the study variable with the known median of the auxiliary variable in simple random sampling.Materials and Methods: The bias and mean squared error of proposed estimator are derived and compared with that of the existing estimators by analytically and numerically.Results: The proposed estimator is less biased and mean squared error is less than that of the existing estimators and from the numerical study, under some known natural populations, the bias of proposed estimator is approximately zero and the mean squared error ranged from 6.83 to 66429.21 and percentage relative efficiencies ranged from 103.65 to 2858.75.Conclusion: The proposed estimator under optimum conditions is almost unbiased and performs better than all other existing estimators.Nepalese Journal of Statistics, 2017, Vol. 1, 1-14
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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 (December 11, 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 ridge estimator, restricted almost unbiased ridge estimator and preliminary test estimator. Furthermore, some graphical results are provided to illustrate theoretical results.
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Cebrián, A. Arcos, and M. Rueda García. "Variance estimation using auxiliary information: An almost unbiased multivariate ratio estimator." Metrika 45, no. 1 (January 1997): 171–78. http://dx.doi.org/10.1007/bf02717100.

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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 (January 31, 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 values of α ∈ (0.0, 1.0), the estimators and were found to be best under numerical illustration.
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Rao, T. J., and A. K. P. C. Swain. "A Note on the Hartley-Ross Unbiased Ratio Estimator." Communications in Statistics - Theory and Methods 43, no. 15 (June 30, 2014): 3162–69. http://dx.doi.org/10.1080/03610926.2012.691338.

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Ajith S, Master. "Almost Unbiased Ratio Cum Product Estimator for Finite Population Mean with Known Coefficient of Skewness." Journal of Advanced Research in Applied Mathematics and Statistics 2, no. 1&2 (May 10, 2017): 1–9. http://dx.doi.org/10.24321/2455.7021.201701.

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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, 171–90. Cham: 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 security (2006–2016). We took into account 431 statistical inferences including t-, $$\chi ^2$$ χ 2 -, r-, one-way F-tests, and Z-tests. In addition, we coded the corresponding total sample sizes, group sizes and test families. Given these data, we established the observed effect sizes and evaluated the overall publication bias. We further computed the statistical power vis-à-vis of parametrized population thresholds to gain unbiased estimates of the power distribution.Results. We obtained a distribution of effect sizes and their conversion into comparable log odds ratios together with their standard errors. We, further, gained funnel-plot estimates of the publication bias present in the sample as well as insights into the power distribution and its consequences.Conclusions. Through the lenses of power and publication bias, we shed light on the statistical reliability of the studies in the field. The upshot of this introspection is practical recommendations on conducting and evaluating studies to advance the field.
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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Ratio and regression estimation." In Sampling Theory, 104–39. 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 reduction in overall sampling variance. Model-based prediction offers an alternative to the design-based ratio and regression estimators and we present an overview of this approach. In model-based prediction, the y values associated with population units are viewed as realizations of random variables which are assumed to be related to auxiliary variables according to specified models. The realized values of the target variable are known for the sample, but must be predicted using an assumed model dependency on the auxiliary variable for the non-sampled units in the population. Insights from model-based thinking may assist the design-based sampling theorist in selection of an appropriate estimator. Similarly, we show that insights from design-based estimation may improve estimation of uncertainty in model-based mark-recapture estimation.
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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. Washington, D.C.: 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 the lower bound is shown to be minimum for a pixel-to-image size ratio Δx/σ s of between 1 and 2 over a wide range of signal-to-noise ratios. Similarly for the 2-D problem, the optimum ratio is shown to lie between 1.5 and 2.5. As is customary in direct detection systems, it is also observed that the lower bound is a function of both the signal power and noise power separately and not just their ratio. Finally at high signal-to-noise ratios, the maximum likelihood estimator is shown to be independent of the signal and noise powers.
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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 and hence some intelligent optimization technique is required to find the global minimum. A new hybrid algorithm combining intelligent optimization techniques is proposed. This algorithm includes Enhanced Continuous Tabu Search (ECTS) and an elitism based Genetic Algorithm (GA) which is applied to the parameter estimation problem. ECTS is a modified version of Tabu Search (TS) applied to continuous functions and has an advantage of covering large search spaces. GA is an evolutionary algorithm that has a better convergence towards the optimum solution. The hybrid algorithm combines the respective strengths of ECTS and GA. Simulation results show that the parameters estimated using the proposed algorithm is unbiased in the presence of colored noise.
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