Use of Statistical Models for the Estimation of Abundance from Groundfish Trawl Survey Data

1990 ◽  
Vol 47 (5) ◽  
pp. 894-903 ◽  
Author(s):  
Stephen J. Smith
Keyword(s):  
Statistical Models ◽  
Gadus Morhua ◽  
Finite Population ◽  
Trawl Survey ◽  
Survey Area ◽  
Sample Mean ◽  
Population Mean ◽  
Model Based ◽  
Design Consistency ◽  
Trawl Surveys

Estimates of fish abundance from stratified random trawl surveys are highly variable and a number of estimators from various statistical models have been suggested to provide more precise estimates. However, model-based estimates of the survey finite population mean which are not based on the sample mean, can be biased and nonrobust to deviations from the model. This is demonstrated in particular for estimates based on the Δ-distribution. A criterion known as asymptotic design consistency (ADC) is presented for selecting those models that can provide estimates of the finite population mean which are asymptotically robust to deviations from the model. The concept of a predictive estimate is presented as a means of incorporating models into an estimate of the finite population mean which can provide more information than the sample mean. Predictive estimates use statistical models to relate the abundance measured in the sample to covariates measured over the whole survey area. This paper demonstrates that consistent relationships exist between the catch of age 4 cod (Gadus morhua) in the survey trawl and concurrently measured hydrographic covariates which can be used to construct model-based ADC predictive estimates of the finite population mean.

scholarly journals Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
G. N. Singh ◽  
Mohd Khalid
Keyword(s):  
Linear Combination ◽  
Finite Population ◽  
Auxiliary Information ◽  
Successive Sampling ◽  
Sample Mean ◽  
Population Means ◽  
Population Mean ◽  
Practical Applications ◽  
Optimum Estimator ◽  
Class Of Estimators

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

scholarly journals Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

2020 ◽  
pp. 55-65
Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
Y. A. Erinola ◽  
M. A. Yunusa ◽  
A. Audu ◽  
...  
Keyword(s):  
Random Sampling ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Degree Of Approximation ◽  
Ratio Estimator ◽  
Auxiliary Variables ◽  
Sampling Without Replacement ◽  
Sample Mean ◽  
Population Mean ◽  
Product Estimator

A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.

scholarly journals Dual Use of Auxiliary Information for Estimating the Finite Population Mean under the Stratified Random Sampling Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sohaib Ahmad ◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Uzma Yasmeen ◽  
Javid Shabbir ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Simulation Study ◽  
Random Sampling ◽  
Finite Population ◽  
Stratified Random Sampling ◽  
Data Sets ◽  
Mean Square ◽  
Sample Mean ◽  
Population Mean ◽  
Mean Square Errors

In this paper, we proposed an improved family of estimators for finite population mean under stratified random sampling, which needed a helping variable on the sample mean and rank of the auxiliary variable. The expression of the bias and mean square error of the proposed and existing estimators are computed up to the first-order approximation. The estimators proposed in different situations were investigated and provided a minimum mean square error relative to all other estimators considered. Four actual data sets and simulation studies are carried out to observe the performance of the estimators. For simulation study, R software is used. The mean square errors of all four data sets are minimum and percent relative efficiencies are more than a hundred percent higher than the other existing estimators, which indicated the importance of the newly proposed family of estimators. From the simulation study, it is concluded that the suggested family of estimators achieved better results. We demonstrate theoretically and numerically that the proposed estimator produces efficient results compared to all other contend estimators in entire situations. Overall, we conclude that the performance of the family of suggested estimators is better than all existing estimators.

scholarly journals On the Accuracy of the Sine Power Lomax Model for Data Fitting

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 78-104
Author(s):  
Vasili B. V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Statistical Model ◽  
Simulation Study ◽  
Statistical Models ◽  
Goodness Of Fit ◽  
Data Fitting ◽  
Applied Science ◽  
Survival Distribution ◽  
Lomax Distribution ◽  
Model Based

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

Design-based and model-based inference in survey sampling: appreciating the difference

1998 ◽  
Vol 28 (10) ◽  
pp. 1429-1447 ◽  
Author(s):  
T G Gregoire
Keyword(s):  
Finite Population ◽  
Survey Sampling ◽  
Finite Population Sampling ◽  
Population Sampling ◽  
Model Based ◽  
The Difference

Model-based ideas in finite-population sampling have received renewed discussion in recent years.Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly.The two inferential paradigms are constrasted, andexplanations are supplemented with examples of discrete aswell as continuously distributed populations. The treatment of spatial structureis examined, also.

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