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Relevant bibliographies by topics / Simple random sampling without replacement (SRSWOR)

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Academic literature on the topic 'Simple random sampling without replacement (SRSWOR)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Simple random sampling without replacement (SRSWOR)"

1

Vincent, Odhiambo, Hellen Waititu, and Nyakundi Omwando Cornelious. "Nonparametric Estimation of Error Variance under Simple Random Sampling without Replacement." International Journal of Mathematics And Computer Research 10, no. 10 (2022): 2925–33. http://dx.doi.org/10.47191/ijmcr/v10i10.02.

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This study adopts a nonparametric approach in the estimation of a finite population error variance in the setting where the variance is a constant (homoscedastic) using a model-based technique under simple random sampling without replacement (SRSWOR). A mean square analysis of the estimator has been conducted, including the asymptotic behaviour of the estimator and the results show that the asymptotic distribution in a homoscedastic setting is asymptotically unbiased and consistent. The performance of the developed estimator is compared to that of other existing estimators using real data. R s

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2

Chaudhuri, Arijit, and Sonakhya Samaddar. "Estimating the population mean using a complex sampling design dependent on an auxiliary variable." Statistics in Transition New Series 23, no. 1 (2022): 39–54. http://dx.doi.org/10.2478/stattrans-2022-0003.

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Abstract In surveying finite populations, the simplest strategy to estimate a population total without bias is to employ Simple Random Sampling (SRS) with replacement (SRSWR) and the expansion estimator based on it. Anything other than that including SRS Without Replacement (SRSWOR) and usage of the expansion estimator is a complex strategy. We examine here (1) if from a complex sample at hand a gain in efficiency may be unbiasedly estimated comparing the “rival population total-estimators” for the competing strategies and (2) how suitable model-expected variances of rival estimators compete i

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3

Odhiambo, Vincent, Waititu Hellen, and Omwando Cornelious Nyakundi. "NONPARAMETRIC ESTIMATION OF ERROR VARIANCE UNDER SIMPLE RANDOM SAMPLING WITHOUT REPLACEMENT." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 10, no. 10 (2022): 2925–33. https://doi.org/10.5281/zenodo.7229084.

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This study adopts a nonparametric approach in the estimation of a finite population error variance in the setting where the variance is a constant (homoscedastic) using a model-based technique under simple random sampling without replacement (SRSWOR). A mean square analysis of the  estimator has been conducted, including the asymptotic behaviour of the  estimator and the results show that the asymptotic distribution in a homoscedastic setting is asymptotically unbiased and consistent. The performance of the developed estimator is compared to that of other existing estimators using re

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Onyeka, A. C. "A class of product-type exponential estimators of the population mean in simple random sampling scheme." Statistics in Transition new series 14, no. 2 (2013): 189–200. http://dx.doi.org/10.59170/stattrans-2013-012.

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The present study proposes a class of product-type exponential estimators for estimating the population mean of the study variable, using known values of some population parameters of an auxiliary character, under the simple random sampling without replacement (SRSWOR) scheme. Furthermore, the study also proposes a modified exponential estimator based on both the ratio-type and the product-type exponential estimators. Properties of the proposed estimators, under the SRSWOR scheme, are obtained up to first order approximation. The modified exponential estimator under optimum conditions is shown

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Ahmed, M. S., and Atsu S. S. Dorvlo. "A GENERAL CLASS OF ESTIMATORS UNDER MULTI PHASE SAMPLING." Statistics in Transition new series 10, no. 2 (2009): 183–92. http://dx.doi.org/10.59170/stattrans-2009-015.

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This paper derives the general estimators for finite population mean using multivariate auxiliary information under multiphase sampling. Here a number of auxiliary variables are considered in each phase under general sampling design. The properties of these estimators are studied and the results are presented for simple random sampling without replacement (SRSWOR) scheme. Using a modified cost function the optimum sample sizes are also derived.

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Irfan, Muhammad, Maria Javed, Sandile C. Shongwe, Muhammad Zohaib, and Sajjad Haider Bhatti. "Estimation of Population Median under Robust Measures of an Auxiliary Variable." Mathematical Problems in Engineering 2021 (September 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/4839077.

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In this paper, a generalized class of estimators for the estimation of population median are proposed under simple random sampling without replacement (SRSWOR) through robust measures of the auxiliary variable. Three robust measures, decile mean, Hodges–Lehmann estimator, and trimean of an auxiliary variable, are used. Mathematical properties of the proposed estimators such as bias, mean squared error (MSE), and minimum MSE are derived up to first order of approximation. We considered various real-life datasets and a simulation study to check the potentiality of the proposed estimators over th

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7

IJAZ, MUHAMMAD, TOLGA ZAMAN, BUSHRA HAIDER, and SYED MUHAMMAD ASIM. "AN EFFICIENT CLASS OF PRODUCT ESTIMATORS USING MEASURES OF DISPERSIONS." Journal of Science and Arts 21, no. 2 (2021): 347–54. http://dx.doi.org/10.46939/j.sci.arts-21.2-a03.

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The study suggests a class of product estimators for estimating the population mean of variable under investigation in simple random sampling without replacement (SRSWOR) scheme when secondary information on standard deviation, mean deviation, and quartile deviation is available. The expression for Bias and Mean Square Error (MSE) has been derived. A comparison is made both theoretically and numerically with other existing product estimators. It is concluded that compared to other product type estimators, suggested class of estimators estimate the population mean more efficiently.

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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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Singh, Housila P., Lakshmi N. Upadhyaya, and Premchandra. "AN IMPROVED VERSION OF REGRESSION RATIO ESTIMATOR WITH TWO AUXILIARY VARIABLES IN SAMPLE SURVEYS." Statistics in Transition new series 10, no. 1 (2009): 85–100. http://dx.doi.org/10.59170/stattrans-2009-006.

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This paper considers a family of estimators of population mean Y of the variable y under study using information on two auxiliary variables x and z under simple random sampling without replacement (SRSWOR) scheme. The expressions for bias and mean-squared error of the proposed family of estimators are obtained up to the first degree of approximation. In addition to many, Mohanty (1967), Khare and Srivastava (1981) and Upadhyaya and Singh (1984) estimators are identified as particular members of the suggested family. Asymptotic optimum estimator (AOE) in the family is identified with its approx

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Ounrittichai, Nuntida, Patsaporn Utha, Boonyarit Choopradit, and Saowapa Chaipitak. "Performance Comparison of Three Ratio Estimators of the Population Ratio in Simple Random Sampling Without Replacement." International Journal of Analysis and Applications 22 (July 29, 2024): 121. http://dx.doi.org/10.28924/2291-8639-22-2024-121.

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This study aims to compare the efficacy of three ratio estimators for estimating the population ratio in simple random sampling without replacement (SRSWOR). The estimators under consideration are a customary ratio estimator (~R1), a ratio estimator based on a transformed mean estimator (~R2) introduced by Onyeka et al. [1], and a regression-type estimator (~R3) proposed by Onyeka et al. [2]. We assess the performance of these estimators across three distributions (bivariate normal, bivariate Poisson log-normal, and bivariate Cauchy) while varying both correlation coefficients and sample sizes

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Book chapters on the topic "Simple random sampling without replacement (SRSWOR)"

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de Vries, Pieter G. "Simple Random Sampling without Replacement." In Sampling Theory for Forest Inventory. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-71581-5_1.

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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Equal probability sampling." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0003.

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This chapter presents a formal quantitative treatment of material covered conceptually in Chapter 2, all with respect to equal probability with replacement (SWR) and without replacement selection simple random sampling, (SRS) of samples of size n from a finite population of size N. Small sample space examples are used to illustrate unbiasedness of mean-per-unit estimators of the mean, total and proportion of the target variable, y, for SWR and SRS. Explicit formulas for sampling variance indicate how estimator uncertainty depends on finite population variance, sample size and sampling fraction

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Cobo, Beatriz, and Elvira Pelle. "Item Count Technique in Ranked Set Sampling." In Ranked Set Sampling Models and Methods. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7556-7.ch002.

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In situations where the estimation of the proportion of sensitive variables relies on the observations of real measurements that are difficult to obtain, there is a need to combine indirect questioning techniques. In the present work, the authors will focus on the item count technique, with alternative methods of sampling, such as the ranked set sampling. They are based on the idea proposed by Santiago et al., which combines the randomized response technique proposed by Warner together with ranked set sampling. The authors will carry out a simulation study to compare the item count technique u

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Conference papers on the topic "Simple random sampling without replacement (SRSWOR)"

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Mohan, Mahesh, and Claire Monteleoni. "Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/347.

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In this paper we present a framework for spectral clustering based on the following simple scheme: sample a subset of the input points, compute the clusters for the sampled subset using weighted kernel k-means (Dhillon et al. 2004) and use the resulting centers to compute a clustering for the remaining data points. For the case where the points are sampled uniformly at random without replacement, we show that the number of samples required depends mainly on the number of clusters and the diameter of the set of points in the kernel space. Experiments show that the proposed framework outperforms

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Enache, Calcedonia. "Financial Stability of Romanian Households in Light of the COVID-19 Pandemic Shocks." In 9th BASIQ International Conference on New Trends in Sustainable Business and Consumption. Editura ASE, 2023. http://dx.doi.org/10.24818/basiq/2023/09/059.

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The present paper examines the effects of the Covid-19 pandemic on the financial situation of Romanian Households, using a simple random sampling without replacement. For a larger investigation of the survey results, that offers a new perception in looking at issues of financial stability, a binary logistic regression model was applied in order to econometrically quantify the relationship between determinants and respondents' behavior regarding the use of savings to pay bills and credits commitment during the coronavirus pandemic. The results of the model show that the respondents’ household w

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