Department of Quantitative Methods

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https://escholar.umt.edu.pk/handle/123456789/1162

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Browsing Department of Quantitative Methods by Subject "Mixture Regression"

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    Mixture regression cum ratio estimators of population mean under stratified random sampling
    (University of Management and Technology Lahore, 2017) Iqbal, Kanwal
    In this thesis, single phase Mixture Regression cum ratio Estimators by using auxiliary variables and auxiliary attributes simultaneously have been proposed under Stratified Random Sampling. Special cases of the estimator are discussed and their mean square errors are also derived mathematically. A simulation technique has been used to observe the properties of proposed estimator which shows that the distribution of proposed estimator approximately normal.. An empirical study has been conducted by incorporating quantitative and qualitative characteristics in the form of auxiliary attributes and variables simultaneously to compare the performance of proposed estimator. Comparisons are made with Moeen et al. (2012) single phase mixture regression cum ratio estimator under simple random sampling. It has been found that the mixture regression cum ratio estimator using multiple auxiliary variables and attributes simultaneously under stratified random sampling is more efficient than Moeen et al., (2012) mixture regression cum ratio estimator under simple random sampling
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    Mixture regression estimators of population mean under stratified random sampling
    (University of Management and Technology Lahore, 2017) Fatima, Madiha
    In this study, Mixture Regression Estimators for single phase sampling under stratified random sampling have been proposed, by incorporating the simultaneous use of information on auxiliary variables and attributes. The estimators have been proposed for three different cases and their mean square errors have been derived mathematically. A Simulation study has been done by using simulated data, to check the distribution of proposed estimators. This study shows that proposed estimators, seems to follow the normal distribution. An empirical study has also been done by considering two natural data sets. Mean square errors (MSE’s) for the proposed estimators have also been computed and Efficiency comparisons made with single phase mixture regression estimators proposed by Moeen et al. (2012). On the basis of MSE’s computed through simulation and empirical studies, it is to be concluded that proposed estimators are more efficient than that of estimators proposed by Moeen et al. (2012) for simple random sampling.