scholarly journals Ratio Estimators Using Coefficient of Variation and Coefficient of Correlation

2014 ◽  
Vol 8 (5) ◽  
pp. 70 ◽  
Author(s):  
Prayad Sangngam
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Random Sampling ◽  
Size Estimation ◽  
Auxiliary Variables ◽  
Coefficient Of Correlation ◽  
Ratio Estimators ◽  
Mean Square Errors

This paper introduces ratio estimators of the population mean using the coefficient of variation of  study variable and auxiliary variables together with the coefficient of correlation between the study and auxiliary variables under simple random sampling and stratified random sampling. These ratio estimators are almost unbiased. The mean square errors of the estimators and their estimators are given. Sample size estimation in both sampling designs are presented. An optimal sample size allocation in stratified random sampling is also suggested. Based on theoretical study, it can be shown that these ratio estimators have smaller MSE than the unbiased estimators. Moreover, the empirical study indicates that these ratio estimators have smallest MSE compared to the existing ones.

scholarly journals Difference-Cum-Ratio Estimators for Estimating Finite Population Coefficient of Variation in Simple Random Sampling

2021 ◽  
pp. 13-29
Author(s):  
A. Audu ◽  
M. A. Yunusa ◽  
O. O. Ishaq ◽  
M. K. Lawal ◽  
A. Rashida ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Population Variance ◽  
Study Variable ◽  
Difference Type ◽  
Ratio Estimators ◽  
Mean Square Errors ◽  
Two Populations

In this paper, three difference-cum-ratio estimators for estimating finite population coefficient of variation of the study variable using known population mean, population variance and population coefficient of variation of auxiliary variable were suggested. The biases and mean square errors (MSEs) of the proposed estimators were obtained. The relative performance of the proposed estimators with respect to that of some existing estimators were assessed using two populations’ information. The results showed that the proposed estimators were more efficient than the usual unbiased, ratio type, exponential ratio-type, difference-type and other existing estimators considered in the study.

scholarly journals Analysis of the Impact of the Type of Sampling of Representative Properties on the Results of Mass Appraisal

2020 ◽  
Vol 20 (2) ◽  
pp. 152-167
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Multiple Regression Model ◽  
Stratified Sampling ◽  
Sampling Errors ◽  
Real Estate Valuation ◽  
Simple Sampling ◽  
The Impact ◽  
Mean Square Errors

Abstract Research background: Mass valuation is a process in which many properties are valued simultaneously with a uniform approach. An example of a procedure used for mass real estate valuation is the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA), which can be developed into a multiple regression model. The algorithm is based on a set of drawn representative properties. This set determines, inter alia, the quality of obtained valuations. Purpose: The objective of the study is to verify the hypothesis whether changing the method of sampling representative properties from the originally used simple random sampling to stratified sampling improves the results of the SAREMA econometric variant. Research methodology: The article presents a study that uses two methods of representative properties sampling – simple random sampling and stratified sampling. Errors of the models of valuation created taking into account both methods of sampling and different number of representative properties are compared. A key aspect of the survey is the choice of a better sampling method. Results: The study has shown that stratified sampling improves valuation results and, more specifically, allows for lower root mean square errors. Stratified sampling yielded better results in the initial phase of the study with more observations, but reducing the percentage of strata participating in the draws, despite the increase in RMSE, guaranteed lower errors than the corresponding results based on simple sampling in all variants of the study. Novelty: The article confirms the possibility of improving the results of mass property valuation by changing the scheme of representative properties sampling. The results allowed for the conclusion that stratified sampling is a better way of creating a set of representative properties.

scholarly journals SISTEMA DE INVENTÁRIO FLORESTAL PARA SERINGAL NATIVO

FLORESTA ◽  
2002 ◽  
Vol 32 (1) ◽  
Author(s):  
José de Arimatéa Silva ◽  
Sylvio Péllico Netto
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Rubber Tree ◽  
Basal Area ◽  
Simple Random Sampling ◽  
Inventory System ◽  
Stratified Random Sampling ◽  
Stage Number ◽  
Second Stage ◽  
Two Stages

Este trabalho teve como objetivo desenvolver um Sistema de Inventário Florestal para seringal nativo. Aplicou-se a Amostragem Inteiramente Aleatória (AIA), em dois estágios: colocação de seringa, no primeiro, e estrada de seringa, no segundo. Foram estimados: número de seringueiras por estrada (N), área basal das seringueiras da estrada (G) e volume da porção explorada do fuste (V). Realizou-se uma pós-estratificação, considerando-se estradas de centro e de margem, aplicando-se a Amostragem Estratificada (AE). Comparou-se a AIA com a AE, com base na eficiência relativa. Os resultados revelaram as seguintes estimativas para as médias estratificadas: N=100; G=19,00 m², V= 62,8 m³. Concluiu-se que a AE revelou-se mais eficiente que a AIA para estimar as variáveis analisadas. Propõe-se que um sistema de inventário para seringal nativo deve combinar: informações de um censo das colocações; um processo de amostragem estratificada; e um método de amostragem cuja unidade de amostra é a estrada de seringa. Forest Inventory System for Rubber Trees Abstract Forest Inventory System for rubber trees. This work had as objective to develop an Inventory System for native rubber tree areas. The Simple Random Sampling (SRS) was applied in two stages: the setting, in the first, and the rubber trees tracks, in the second stage. Number of rubber trees per track (N), basal area of the rubber trees track (G) and volume of the stem portion explored (V) were the parameters estimated. A post-stratification was become fulfilled, considering itself center tracks and river side tracks, applying itself it Stratified Random Sampling (STRS). It was compared SRS with the STRS, on the basis of the relative efficiency. The results showed the following estimates for the stratified means: N=100; G=19,00 m², V = 62,8 m³. It was concluded that the STRS showed more efficient than the SRS to estimate the analyzed variables. It is considered that an Inventory System for native rubber tree areas must match: information of a census of the settings; a process of Stratified Random Sampling; and a sampling method whose unit of sample is the rubber tree track.

Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling

2022 ◽  
pp. 62-85
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose M. Sautto ◽  
Khalid Ul Islam Rather
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Auxiliary Variables ◽  
Mild Conditions ◽  
Squared Error ◽  
The Mean ◽  
Better Than

This chapter introduced basic elements on stratified simple random sampling (SSRS) on ranked set sampling (RSS). The chapter extends Singh et al. results to sampling a stratified population. The mean squared error (MSE) is derived. SRS is used independently for selecting the samples from the strata. The chapter extends Singh et al. results under the RSS design. They are used for developing the estimation in a stratified population. RSS is used for drawing the samples independently from the strata. The bias and mean squared error (MSE) of the developed estimators are derived. A comparison between the biases and MSEs obtained for the sampling designs SRS and RSS is made. Under mild conditions the comparisons sustained that each RSS model is better than its SRS alternative.

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