Department of Statistics

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Author: search.filters.author.Dr. Riffat JabeenSubject: search.filters.subject.Department of Statistics

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    Development of Estimators for Population Mean of Sensitive Variable in Survey Sampling
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Kamran ASLAM; SP16-RST-005; LHR TP 4833; Dr. Riffat Jabeen
    In Chapter 1, introduction about Survey sampling, use of auxiliary in formation in context stratified random sampling has been deliberated. Further in Chapter 1, Different methods dealing randomized response have been deliberated by different authors but Hansen-Hurwitz sub sampling has been consider in more detail. In Chapter 2, the literature regarding the use of classical sampling de sign has been deliberated. The literature about some existing estimators for calibration estimator under stratified random sampling has been ex press. Various work of randomized response has also been specified in Chapter 2. Some well-known existing randomized response model, scrambling vari able under auxiliary and study variable, with randomized response or complete response have been reproduced in Chapter3. The major con tribution of this study starts from next Chapter. In Chapter 4, new estimators using auxiliary information have been proposed for calibration estimator in randomized response under strat ified random sampling scheme. The mean square error each estimator has been derived along. In Chapter 5 conclusion has been drawn about the generalized class of estimators proposed in this study. Finally it is shown that the propose estimator II is more efficient than proposed estimator I.
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    A study of ordered random variables for Reflected Power Function Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Maydah Azhar; CIIT/FA20-BST-008/LHR; Dr. Riffat Jabeen; LHR TP 9953
    This thesis presents a comprehensive study of ordered random variables for the Reflected Power Function Distribution (RPFD). The primary focus is on the statistical properties, parameter estimation methods, and applications of the RPFD. The RPFD is a transformation of the traditional Power Function Distribution, which is frequently used to model phenomena in various fields such as hydrology, economics, and engineering. By reflecting the distribution around its midpoint, the RPFD provides a mirrored perspective that is particularly useful for scenarios where probabilities are skewed in the opposite direction. The study begins with an introduction to the Power Function Distribution and its reflection to form the RPFD. The properties of the RPFD, including its probability density function, survival function, hazard rate function, and moments, are thoroughly explored. Various parameter estimation techniques such as Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), and percentile-based estimators are discussed in detail. Furthermore, the thesis delves into generalized order statistics (GOS) and their specific application to the RPFD. This includes the derivation of the probability density function (PDF) and joint PDF for GOS, as well as the calculation of mean, variance, and covariance for different scenarios. The literature review highlights key studies and methodologies that have contributed to the development and understanding of order statistics and RPFD. Empirical analysis using real-life data sets demonstrates the practical utility and superior performance of the RPFD compared to other models. Overall, this research contributes to the statistical theory by providing new insights and tools for the application of the RPFD in various scientific and engineering disciplines.
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    Some Memory Type Estimators for Population Variance in Survey Sampling
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Umme Habiba; CIIT/FA23/RST/004/LHR; Dr. Riffat Jabeen; LHR TP 9874
    Reducing and estimating population variation is important in survey sampling. These variations can occur in any sampling design, including stratified random sampling. In stratified random sampling the difference in stratum weights increase the variation. This problem can be control with the use of calibration techniques, and auxiliary information. By using this technique, we can increase the accuracy and efficiency of estimator. Improving the efficiency of estimator for population variance estimation is the primary goal of this study. Both exponentially weighted moving average (EWMA) and extended exponentially weighted moving average (EEWMA) memory type statistics are used in this study to estimate population variance. EWMA use only current data whereas EEWMA use both current and past observations. The EWMA and EEWMA statistics are used to build the calibration variance and ratio estimators. The mean square errors are calculated, and the effectiveness of the estimators is illustrated through a discussion of mathematical comparisons. To assess the efficiency of the proposed memory type estimators, a simulation study is conducted and MSE are compare with existing estimators. The results show that proposed estimators are perform efficiently as compared to the existing estimators