Phd DQM (Economics and Qualitative Management)

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    A Class of Generalized Estimators of Population Mean Under Ranked Set Sampling Schemes
    (UMT, Lahore, 2022) Asad Ali
    An efficient estimate of the population mean plays an important role in the field of sample surveys, especially when population is heterogenous. The main focus of this dissertation is to enhance efficiency of mean estimators for the heterogenous populations. In Chapter 1, a comprehensive discussion about the Simple Random Sampling (SRS), Stratified Random Sampling (StRS), Ranked Set Sampling (RSS), Stratified Ranked Set Sampling (StRSS),Extreme Ranked Set Sampling (ERSS), Paired Ranked Set sampling (PRSS), Median Ranked Set Sampling (MRSS), Double Ranked Set Sampling (DRSS), Stratified Extreme Ranked Set Sampling (StERSS), Stratified Median Ranked Set Sampling (StMRSS), Stratified Double Ranked Set Sampling (StDRSS), Paired Double Ranked Set Sampling (PDRSS), Stratified Paired Double Ranked Set Sampling (StPDRSS), Extreme-cum-Median Ranked Set Sampling (EMRSS) has been given. In Chapter 2, the literature on ranking schemes of RSS and estimators with one or two auxiliary variables have been discussed theoretically. Research gap and objectives of this dissertation have also been identified in this chapter. In Chapter 3, notations, symbols and different mathematical relations have been given. Mathematical forms of existing estimators have also been discussed in this chapter.
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    DEVELOPMENT OF HYBRID CLASSIFIERS FOR CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH LIMITED SAMPLE SIZE
    (UMT, Lahore, 2024) Arzoo Kanwal
    Classification is one of the potential and widely applied domain of statistical modeling. Classifiers have a wide variety of applications in high dimensional data sets which include areas of data mining, natural language processing, finance, voice recognition, signal decoding, medicine, chemometrics, etc. Multicollinearity, Heterogeneity and Non-normality have an impact on how well classifiers work in data sets with many dimensions and tiny sample numbers. The existing hybrid high dimensional classifiers do not work optimally under these conditions. In this work, new Hybrid Classifiers are developed which are robust under the aforementioned assumptions. The consistency and classification accuracy of the estimations are unclear when dealing with such high-dimensional data. Assuming that the multivariate regular distribution of the PLS scores, linear discriminant analysis is typically used in conjunction with PLS scores.
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    Improvements In Statistical Monitoring Methods Using Modified Successive Sampling
    (UMT, Lahore, 2024) Mehvish Hyder
    In practice, the Statistical Process Control (SPC) toolkit is used to detect the shift in process's location and dispersion parameters. In this toolkit, the control charts are the most frequently used and effective tool for the real-time surveillance of a process. A control chart indicates the process's situation, whether in-control or out-of-control, due to special cause variation in the process. In addition, for the quality assessment, samples are generated through simple random sampling; however, to reduce the sampling time and cost, samples are preferably generated through the modified successive sampling (MSS) mechanism. The existing memoryless (Shewhart) control charts based on MSS scheme for location and dispersion parameters monitoring have very poor performance, and not a single study has made on memory-type control charts based on MSS technique. There was a big research gap presents in memory-type control charts under MSS scheme for the monitoring of both location as well as dispersion parameters and this was an open problem which needed to be fill. Thus, by overcoming this problem, we propose some new efficient and cost-effective memory-type control charts based on MSS technique to detect the shift in location and scale parameters in this thesis.
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    MONITORING OF MULTIPLE LINEAR PROFILES BY USING RIDGE REGRESSION ESTIMATORS
    (UMT, Lahore, 2023) Muhammad Faran
    In many quality control studies the performance of a product or process is usually characterized by a single response variable however, in some applications of quality control, the performance of a product or a process can be best characterized by a linear relationship between a response variable and one or more explanatory variables (Noorossana et al., 2011). But, when more than one explanatory variables are involved in the profile it may indicate the presence of high collinearity among explanatory variables, which is called multicollinearity (Gujarati, 2022). It should be noted that if the multicollinearity is neglected during the profile monitoring, then the designed control charts applied in phase II monitoring, provide lack of the sufficient effectiveness in detecting shifts or out of control signals. In this thesis, the effect of the multicollinearity has been observed on the monitoring of multiple linear profiles and propose some novel control charts (EWMA, Shewhart and Shewhart_3) for Intercept, Slopes and Mean Squared Error (MSE)/Error Variance by using Ridge Regression (RR) estimators in order to provide the solution of multicollinearity. An application of wind tunnel data by NASA Langley Research Centre has been used. The performance of the proposed novel control charts have been evaluated by using the Average Run Length (ARL) criterion. The results indicated that in the presence of high multicollinearity the proposed novel control charts for Intercept, Slopes and MSE/Error Variance based on RR estimators are outperform as compare to the traditional existing control charts based on Ordinary Least Squared (OLS) estimator.
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    NEW ESTIMATION METHODS TO HANDLE ULTICOLLINEARITY AND DISPERSION ISSUES IN CONWAY MAXWELL’S POISSON REGRESSIONMODELLING
    (UMT, Lahore, 2024) Faiza Sami
    The count regression model is widely used in real life. The model is less precise in the presence of multicollinearity and dispersion. The flexible model with biased estimation method is an appropriate. The Conway-Maxwell Poisson Regression model (COMPRM) is used to handle dispersion issues and biased estimation methods resolve the problem of multicollinearity. COMPRM can resolve the problem of over, under and equi-dispersion cases by using additional dispersion parameter. As, COMPRM uses mean and dispersion parameter in modelling by using two GLM. In this study, we use mean model for estimation with all three cases of dispersion. Beside this, biased estimation methods provide an estimator that give efficient performance as compared to traditional method of estimation. To accomplish the objectives of the study we conduct simulation study under various conditions and two real life applications. We proposed a new generalized ridge estimator, generalized liu estimator, generalized modified one parameter liu estimator and generalized almost unbiased ridge estimator for COMPRM.
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    Sample Size and Interval Estimation in Survey Sampling by Using Generalized Mixture Estimators under Simple and Stratified Sampling
    (UMT, Lahore, 2020) Kanwal Iqbal
    In the field of sample surveys, an accurate estimate of the population mean is crucial, particularly in cases where the population is heterogeneous. The use of auxiliary information enhances the efficiency of the estimator. To keep this feature in mind, we combine the use of auxiliary variables and attributes to enhance the efficiency of mean estimators for homogeneous and heterogeneous populations. In Chapter 1, a comprehensive discussion about simple random sampling (SRS), stratified random sampling (StRS), the use of auxiliary variables, and the basic concepts of classical ratio, product, and regression estimators has been given. Further, the sample size estimation, design effect, and confidence interval have also been discussed. In Chapter 2, the literature on simple random sampling (SRS) and stratified random sampling (StRS) schemes and estimators with one or two auxiliary variables and attributes has been discussed theoretically. The research gap, significance of the study, and objectives of this dissertation have also been identified in this chapter.