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About 10,000 papers for “Simple random sample”

Simple Random Sample

Roger E. Kirk · International Encyclopedia of Statistical Science · 2011

Cited by 13

On conditional simple random sample

Janusz Wywiał · Statistics in Transition New Series · 2014

Estimation of the population average in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics of the auxiliary variable is studied. The sampling scheme implementing the sampling design is proposed. The inclusion probabilities are derived. The well known Horvitz-Thompson statistic under the conditional simple random sampling designs is considered as the estimator of population mean. Moreover, it was shown that the Horvitz-Thompson estimator under some particular cases of the conditional simple random sampling design is more accurate than the ordinary mean from the simple random sample.

Cited by 2Open access

Combined Analysis of Overlapping Stratified Random Sample and Simple Random Sample Data

Michael S. Hamada, Kenneth J. Ryan · Quality and Reliability Engineering International · 2014

This article provides a methodology to combine overlapping stratified random samples and a simple random sample to estimate subpopulation proportions. That is, all the available data can be used to better estimate the quantities of interest. The methodology based on a Bayesian approach is illustrated with a synthetic data set. WinBUGS code that implements the combined analysis is also presented. Copyright © 2014 John Wiley & Sons, Ltd.

Cited by 2

Calibration-Weighting a Stratified Simple Random Sample with SUDAAN

Phillip S. Kott · 2022

This report shows how to apply the calibration-weighting procedures in SAS-callable SUDAAN (Version 11) to a stratified simple random sample drawn from a complete list frame for an establishment survey. The results are calibrated weights produced via raking, raking to a size variable, and pseudo-optimal calibration that potentially reduce and appropriately measure the standard errors of estimated totals. The report then shows how to use these procedures to remove selection bias caused by unit nonresponse under a plausible response model. Although unit nonresponse is usually assumed to be a function of variables with known population or full-sample estimated totals, calibration weighting can often be used when nonresponse is assumed to be a function of a variable known only for unit respondents (i.e., not missing at random). When producing calibrated weights for an establishment survey, one advantage the SUDAAN procedures have over most of their competitors is that their linearization-based variance estimators can capture the impact of finite-population correction.

Cited by 1Open access

On Treating A Survey Of Convenience Sample As A Simple Random Sample

W. Gregory Thatcher, J. Wanzer Drane · Journal of Modern Applied Statistical Methods · 2003

Threat of bias has kept many from using data gathered in less than optimal conditions. We maintain that when convenience sampling represents race and gender at nearly correct proportions and can be beneficial, as these two variables are quite often used as stratification variables. We compared a convenience sample with a proven sample. Race and Sex were nearly proportional as was found in the proven sample. We conclude that the convenience sample can be used as though it is simple random.

Cited by 1Open access

Simple Random Sample of all birth certificates in Mexico 2010-2023

Lamadrid-Figueroa, Hector · Figshare · 2025

<b>Background:</b><br>C-section rates in Mexico have risen dramatically over the past decade, reaching 55% by 2023. While life-saving when medically indicated, excessive use may alter the natural timing of childbirth and shift population-level birth outcomes.<b>Objectives:</b><br>To examine trends in gestational age, birth weight, and birth length from 2010 to 2023, and evaluate their association with mode of delivery.<b>Design:</b><br>Population-based trend analysis using national birth certificate data.<b>Methods:</b><br>We analyzed a 1-in-1,000 random sample of birth certificates from Mexico’s national registry (n = 27,320) for 2010–2023.

Cited by 0Open access

Estimation of mean on the basis of conditional simple random sample

Janusz Wywiał · Statistics in Transition New Series · 2016

Estimation of the population mean in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics (quantiles) of an auxiliary variable is considered. Properties of the well-known Horvitz-Thompson and ratio type estimators as well as the sample mean are taken into account under the conditional simple random sampling designs. The considered examples of empirical analysis lead to the conclusion that under some additional conditions the proposed estimation strategies based on the conditional simple random sample are usually more accurate than the mean from the simple random sample drawn without replacement.

Cited by 0Open access

Simple Random Sample of all birth certificates in Mexico 2010-2023

Lamadrid-Figueroa, Hector · Figshare · 2025

<b>Background:</b><br>C-section rates in Mexico have risen dramatically over the past decade, reaching 55% by 2023. While life-saving when medically indicated, excessive use may alter the natural timing of childbirth and shift population-level birth outcomes.<b>Objectives:</b><br>To examine trends in gestational age, birth weight, and birth length from 2010 to 2023, and evaluate their association with mode of delivery.<b>Design:</b><br>Population-based trend analysis using national birth certificate data.<b>Methods:</b><br>We analyzed a 1-in-1,000 random sample of birth certificates from Mexico’s national registry (n = 27,320) for 2010–2023.

Cited by 0Open access

Estimation of Mean on the Basis of Conditional Simple Random Sample

Janusz Wywiał · Statistics in Transition New Series · 2016

Abstract Estimation of the population mean in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics (quantiles) of an auxiliary variable is considered. Properties of the well-known Horvitz-Thompson and ratio type estimators as well as the sample mean are taken into account under the conditional simple random sampling designs. The considered examples of empirical analysis lead to the conclusion that under some additional conditions the proposed estimation strategies based on the conditional simple random sample are usually more accurate than the mean from the simple random sample drawn without replacement.

Cited by 0Open access

The Sample Size Determination Strategy in the Simple Random Sampling Design

Hanaw Amin · Tikrit Journal of Administrative and Economic Sciences · 2024

his paper presents an overview for some strategies concerning the sample size determination including simple random sampling technique in the applied field of science. For this purpose, we introduce details of most commonly used formula for estimating the sample size in the case of dichotomous and continuous outcomes with presenting several effective criteria are affect the size determination. These factors are sampling error or (level of precision), confidence interval CI, and degree of variability (heterogeneity) between unites, proportion of event occurrence (p), population size, and study design. In general level of precision is pointed as ±10% for governmental politics voting survey, ±5% for marketing study, and ±1% for health and industrial purpose survey. This measure is affected by the sample size and can be denoted by SEM if descriptively computed and inferentially can be calculated by (t- test) statistics. In the case of dichotomous outcomes, the proportion of success (p) needs to be stated carefully, because the sample size directly affected by any increasing or decreasing changes volume. Beside these criteria presenting an outline of eight generally used techniques precisely to defined the sample size. We conclude that one technique for determining the sample size is not appropriate for solving all problems. A Specific research problem are needs some specific sample size techniques since initially they based on three different measurements of data (ordinal, nominal or ratio).

Cited by 0Open access

A Simple Sample-Mounting Method for Random Powder X-Ray Diffraction

Guoping Zhang, John T. Germaine, Robert T. Martin et al. · Clays and Clay Minerals · 2003

Abstract This paper describes an improved simple, sample-mounting method for random powder X-ray diffraction (XRD), namely the razor tamped surface (RTS) method, which prepares a powder mount by tamping the loose powder with the sharp edge of a razor blade. Four kaolinites and a quartz powder were used to evaluate the RTS method by quantifying the degree of orientation in the sample mounts using orientation indices. Comparisons between the RTS and other published simple methods, consisting of front loading, back loading and side loading, indicate that the RTS method produces minimum packing density and minimum preferred orientation in the powder mounts of all five samples. The quartz powder used in this study does exhibit a tendency to preferred orientation. The mechanism by which the RTS method reduces preferred orientation is examined by comparing the width of the sharp blade edge with the size of clay particles. The advantages and disadvantages of the RTS method are also discussed.

Cited by 96

On the Estimation of Error by Simple Random Sample Formula in Complex Sample Designs

Z. A. El‐Beshir, M.Y. Radwan · The Egyptian Statistical Journal/The Egyptian Statistical Journal · 1991

This paper discusses the consequence of using simple random sample formula instead of the proper formulae to estimate error in complex sample designs. Emphasis is placed on determining the conditions under which this procedure can lead to misleading conclusions. A simulation study confirmed the (analytical) established conditions and suggested new ones.

Cited by 0

On simple wavelet estimators of random signals and their small-sample properties

Joanna Bruzda · Journal of Statistical Computation and Simulation · 2014

In the paper we suggest certain nonparametric estimators of random signals based on the wavelet transform. We consider stochastic signals embedded in white noise and extractions with wavelet denoizing algorithms utilizing the non-decimated discrete wavelet transform and the idea of wavelet scaling. We evaluate properties of these estimators via extensive computer simulations and partially also analytically. Our wavelet estimators of random signals have clear advantages over parametric maximum likelihood methods as far as computational issues are concerned, while at the same time they can compete with these methods in terms of precision of estimation in small samples. An illustrative example concerning smoothing of survey data is also provided.

Cited by 4

RFEX: Simple Random Forest Model and Sample Explainer for non-Machine Learning experts

Dragutin Petković, Amir Alavi, Derun Cai et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019

Machine Learning (ML) is becoming an increasingly critical technology in many areas. However, its complexity and its frequent non-transparency create significant challenges, especially in the biomedical and health areas. One of the critical components in addressing the above challenges is the explainability or transparency of ML systems, which refers to the model (related to the whole data) and sample explainability (related to specific samples). Our research focuses on both model and sample explainability of Random Forest (RF) classifiers. Our RF ex plainer, RFEX, is designed from the ground up with non-ML experts in mind, and with simplicity and familiarity, e.g. providing a one-page tabular output and measures familiar to most users. In this paper we present significant improvement in RFEX Model explainer compared to the version published previously, a new RFEX Sample explainer that provides explanation of how the RF classifies a particular data sample and is designed to directly relate to RFEX Model explainer, and a RFEX Model and Sample explainer case study from our collaboration with the J. Craig Venter Institute (JCVI). We show that our approach offers a simple yet powerful means of explaining RF classification at the model and sample levels, and in some cases even points to areas of new investigation. RFEX is easy to implement using available RF tools and its tabular format offers easy-to-understand representations for non-experts, enabling them to better leverage the RF technology.

Cited by 0Open access