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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Un estimador logarítmico eficiente en muestreo aleatorio estratificado utilizando una única variable auxiliar

Fazal Shakoor1, Muhammad Asif2, Muhammad Atif3

  • 1Department of Statistics, University of Peshawar, Peshawar, Pakistan.

Scientific reports
|February 26, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo estimador de tipo logarítmico mejora la estimación de la media poblacional en muestreo aleatorio estratificado utilizando variables auxiliares. Este novedoso enfoque mejora la precisión y eficiencia de la encuesta, superando a los métodos existentes en simulaciones y análisis de datos del mundo real.

Palabras clave:
AuxiliarSesgoEficienciaEstimador logarítmicoMSEPREEstimador de razónMuestreo aleatorio estratificado

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Área de la Ciencia:

  • Estadística
  • Metodología de Encuestas

Sus antecedentes:

  • La estimación precisa de las medias poblacionales es crucial en las encuestas estadísticas.
  • El muestreo aleatorio estratificado es una técnica común, a menudo mejorada por información auxiliar.
  • Los estimadores existentes pueden no aprovechar al máximo las variables auxiliares para mejorar la precisión.

Objetivo del estudio:

  • Proponer un novedoso estimador de tipo logarítmico para la estimación de la media poblacional.
  • Mejorar la utilización de una única variable auxiliar en el muestreo aleatorio estratificado.
  • Mejorar la precisión y eficiencia de la estimación en comparación con los métodos existentes.

Principales métodos:

  • Desarrollo de un estimador de tipo logarítmico.
  • Derivación de expresiones analíticas para el sesgo y la precisión.
  • Evaluación empírica utilizando conjuntos de datos de la vida real y estudios de simulación.
  • Comparación con estimadores existentes bajo diferentes tamaños de muestra (n=50, 100, 150).

Principales resultados:

  • El estimador propuesto demuestra una mayor eficiencia porcentual relativa.
  • Se observó consistentemente una precisión superior en conjuntos de datos reales y simulados.
  • Se establecieron condiciones teóricas para que el estimador propuesto domine a los existentes.
  • Se notaron mejoras significativas en la precisión de la encuesta, especialmente con una fuerte asociación de variables.

Conclusiones:

  • El novedoso estimador de tipo logarítmico ofrece mejoras sustanciales en la precisión de la encuesta.
  • La explotación eficaz de la información auxiliar conduce a una mayor precisión en la estimación.
  • El método propuesto es un avance valioso para los estadísticos de encuestas que utilizan variables auxiliares.