scholarly journals Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Awadhesh K. Pandey ◽  
G. N. Singh ◽  
D. Bhattacharyya ◽  
Abdulrazzaq Q. Ali ◽  
Samah Al-Thubaiti ◽  
...  
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Real Life ◽  
Mean Square ◽  
Extensive Simulation ◽  
Gamma Distributions ◽  
Using Data ◽  
Point Estimators ◽  
Mean Square Errors ◽  
Logarithmic Type

In this manuscript, three new classes of log-type imputation techniques have been proposed to handle missing data when conducting surveys. The corresponding classes of point estimators have been derived for estimating the population mean. Their properties (Mean Square Errors and bias) have been studied. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions, as well as real dataset, has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

scholarly journals An Exponential-Cum-Sine-Type Hybrid Imputation Technique for Missing Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
D. Bhattacharyya ◽  
G.N. Singh ◽  
Taghreed M. Jawa ◽  
Neveen Sayed-Ahmed ◽  
Awadhesh K. Pandey
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Real Life ◽  
Mean Square ◽  
Extensive Simulation ◽  
Population Mean ◽  
Gamma Distributions ◽  
Point Estimator ◽  
Using Data ◽  
Mean Square Errors

In this study, a new exponential-cum-sine-type hybrid imputation technique has been proposed to handle missing data when conducting surveys. The properties of the corresponding point estimator for population mean have been examined in terms of bias and mean square errors. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

Exact confidence limits for prevalence of a disease with an imperfect diagnostic test

2010 ◽  
Vol 138 (11) ◽  
pp. 1674-1678 ◽  
Author(s):  
J. REICZIGEL ◽  
J. FÖLDI ◽  
L. ÓZSVÁRI
Keyword(s):  
Confidence Intervals ◽  
Diagnostic Test ◽  
Sensitivity And Specificity ◽  
Simulation Study ◽  
Confidence Level ◽  
Asymptotic Methods ◽  
Real Life ◽  
Disease Status ◽  
Confidence Limits ◽  
Extensive Simulation

SUMMARYEstimation of prevalence of disease, including construction of confidence intervals, is essential in surveys for screening as well as in monitoring disease status. In most analyses of survey data it is implicitly assumed that the diagnostic test has a sensitivity and specificity of 100%. However, this assumption is invalid in most cases. Furthermore, asymptotic methods using the normal distribution as an approximation of the true sampling distribution may not preserve the desired nominal confidence level. Here we proposed exact two-sided confidence intervals for the prevalence of disease, taking into account sensitivity and specificity of the diagnostic test. We illustrated the advantage of the methods with results of an extensive simulation study and real-life examples.

Evaluation of alternative methods to predict the lean meat percentage of pig carcasses

2009 ◽  
Vol 2009 ◽  
pp. 106-106 ◽  
Author(s):  
E Magowan ◽  
M E E McCann ◽  
B W Moss ◽  
D Kilpatrick
Keyword(s):  
Root Mean Square ◽  
Cross Sections ◽  
Alternative Methods ◽  
Mean Square ◽  
Prediction Equations ◽  
Lean Meat ◽  
Alternative Techniques ◽  
Using Data ◽  
Pig Carcasses ◽  
Mean Square Errors

The value of a pig carcass is largely based on its lean meat percentage (LM%). The ultimate method of accurately measuring the lean meat percentage is a full dissection of the carcass. However, this is both time consuming and expensive. Lean meat percentage prediction equations have been established for various ‘probes’ and indicator cuts. Although these prediction equations have low (less than 2.5) Root Mean Square Errors of Prediction (RMSEP) and high R_a2 values, research continues to investigate other methods to produce equations with greater accuracy and to identify alternative techniques to full dissection. The aim of this experiment was to investigate the accuracy of lean meat percentage prediction equations using data gathered from 1) grading probes, 2) primal cuts and 3) photographs from cross sections of the loin.

scholarly journals Overcoming the problems caused by collinearity in mixed-effects logistic model: determining the contribution of various types of violence on depression in pregnant women

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanaz Khalili ◽  
Javad Faradmal ◽  
Hossein Mahjub ◽  
Babak Moeini ◽  
Khadijeh Ezzati-Rastegar
Keyword(s):  
Pregnant Women ◽  
Simulation Study ◽  
Fixed Effects ◽  
Logistic Model ◽  
Mixed Effects ◽  
Mean Square ◽  
Empirical Power ◽  
Binary Responses ◽  
Correlated Binary Responses ◽  
Mean Square Errors

Abstract Background Collinearity is a common and problematic phenomenon in studies on public health. It leads to inflation in variance of estimator and reduces test power. This phenomenon can occur in any model. In this study, a new ridge mixed-effects logistic model (RMELM) is proposed to overcome consequences of collinearity in correlated binary responses. Methods Parameters were estimated through penalized log-likelihood with combining expectation maximization (EM) algorithm, gradient ascent, and Fisher-scoring methods. A simulation study was performed to compare new model with mixed-effects logistic model(MELM). Mean square error, relative bias, empirical power, and variance of random effects were used to evaluate RMELM. Also, contribution of various types of violence, and intervention on depression among pregnant women experiencing intimate partner violence(IPV) were analyzed by new and previous models. Results Simulation study showed that mean square errors of fixed effects were decreased for RMELM than MELM and empirical power were increased. Inflation in variance of estimators due to collinearity was clearly shown in the MELM in data on IPV and RMELM adjusted the variances. Conclusions According to simulation results and analyzing IPV data, this new estimator is appropriate to deal with collinearity problems in the modelling of correlated binary responses.

scholarly journals Regression-in-Ratio Estimators for Population Mean by Using Robust Regression in Two Phase Sampling

2021 ◽  
Author(s):  
Aamir Raza ◽  
Muhammad Noor-ul-Amin
Keyword(s):  
Simulation Study ◽  
Robust Regression ◽  
Real Life ◽  
Least Square Method ◽  
Least Square ◽  
Two Phase ◽  
Extensive Simulation ◽  
Population Mean ◽  
Phase Sampling ◽  
Two Phase Sampling

The estimation of population mean is not meaningful using ordinary least square method when data contains some outliers. In the current study, we proposed efficient estimators of population mean using robust regression in two phase sampling. An extensive simulation study is conduct to examine the efficiency of proposed estimators in terms of mean square error (MSE). Real life example and extensive simulation study are cited to demonstrate the performance of the proposed estimators. Theoretical example and simulation studies showed that the suggested estimators are more efficient than the considered estimators in the presence of outliers.

scholarly journals Generalised Regression Estimation Given Imperfectly Matched Auxiliary Data

2021 ◽  
Vol 37 (1) ◽  
pp. 239-255
Author(s):  
Li-Chun Zhang
Keyword(s):  
Simulation Study ◽  
Auxiliary Information ◽  
Survey Sampling ◽  
Regression Estimation ◽  
Regression Estimator ◽  
Mean Square ◽  
Auxiliary Data ◽  
Mean Square Errors

Abstract Generalised regression estimation allows one to make use of available auxiliary information in survey sampling. We develop three types of generalised regression estimator when the auxiliary data cannot be matched perfectly to the sample units, so that the standard estimator is inapplicable. The inference remains design-based. Consistency of the proposed estimators is either given by construction or else can be tested given the observed sample and links. Mean square errors can be estimated. A simulation study is used to explore the potentials of the proposed estimators.

scholarly journals REGRESSION-IN-RATIO ESTIMATORS IN THE PRESENCE OF OUTLIERS BASED ON REDESCENDINGM-ESTIMATOR

2019 ◽  
pp. 01-10
Author(s):  
Aamir Raza ◽  
Muhmmad Noor-ul-Amin ◽  
Muhammad Hanif
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Simulation Study ◽  
Relative Efficiency ◽  
Mean Square ◽  
Extensive Simulation ◽  
Taylor Series Approximation ◽  
Series Approximation ◽  
M Estimator ◽  
Ratio Estimators

In this paper, a robust redescending M-estimator is used to construct the regression-inratio estimators to estimate population when data contain outliers. The expression of mean square error of proposed estimators is derived using Taylor series approximation up to order one. Extensive simulation study is conducted for the comparison between the proposed and existing class of ratio estimators. It is revealed form the results that proposed regression-in-ratio estimators have high relative efficiency (R.E) as compared to previously developed estimators. Practical examples are also cited to validate the performance of proposed 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.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

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