M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/63

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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End date: 2019Subject: search.filters.subject.Statistics, computer and systems science

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Now showing 1 - 9 of 9
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    Generalized Ratio Type Exponential Estimators for the Population Mean Using Ranked Set Sampling
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Amjad Ali; SP16-RST-002; LHR TP 4837; Dr. Muhammad Ismail
    The dominance of using ranked set sampling, for estimating the mean of a population, as compare to simple random sampling is well reputable. This technique is useful when visual ordering of a small set of size “m” can be made simply and precisely, since exact measurement of a value is most of the times very hard to obtain. In this dissertation, exponential ratio type estimators are proposed for evaluating the population mean by using ranked set sampling. We found through numerical results that the results of proposed ratio type estimators which is estimated under ranked set sampling is more reliable and competent than the simple random sampling (SRS) and some other already existed estimators under SRS are suggested by Cochran (1977), Parsad 1989, Bahl and Tuteja (1991). The comparison is also made with estimators under ranked set sampling by Samawi and Muttalak (1996), Rabail (2014) and Gajendra , Zeeshan and Carlos (2017).
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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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    Estimation of Population Mean of a Sensitive Variable under Systematic Sampling
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Muhammad Kamran Khan; FA16-RST-002; LHR TP 5310
    In this research study, we consider the problem of mean estimation of a sensitive variable under systematic sampling. The estimator based on one auxiliary variable is proposed for the sensitive variable under systematic sampling. Further, estimators are studied in the presence of non-response. The mean square error (MSE) expressions are derived and efficiency of proposed estimator is proved by conducting simulations.
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    Estimation of Population Mean by Using Robust Regression
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Salah ud din Asghar; SP16-RST-003; LHR TP 5250; Dr. Muhammad Noor-ul-amin
    In the linear regression problems, ordinary least square (OLS) estimates violate in the presence of outliers as according to the assumptions of the OLS model that there should not be outliers in the data. More specifically, since the classical procedures are not applicable to the case of outliers presence. So, for the purpose of estimation there is another approach which handles this situation of outliers that is robust regression. Robust regression is an important tool for investigating data that are tainted with outliers and provide efficient results. In robust regression, estimates completely reject the effect of outliers and provide good estimates of the population parameter. In this work we focus on the problem of outliers occur in the data which provides a bad estimate of the population mean. In this dissertation, redescending M-estimator and ratio estimator have been proposed to estimate finite population mean using the information from auxiliary variable. Discussions have been made about the robust estimation method based on M-estimation, outliers, ratio estimators and auxiliary variable in Chapter 1. The literature about the robust regression and ratio estimator is discussed in Chapter 2. This research work is based on the two main chapters. The 3rd and 4th Chapters are the most important chapters of the thesis. For the first strategy, in the 3rd Chapter a redescending M-estimator is proposed by reworking on the redescending M-estimator proposed by Insha-ullah et al. (2006) which assign a weight closer to zero to the extreme values and closer to one to the good observations. Most of the redescending M-estimators are solved by iteratively reweighted least square method. For the second strategy we adopt the ratio estimators proposed by Kadilar and Cingi (2004) in the 4th Chapter and replace OLS coefficient with our redescending M-estimator proposed in 3rd Chapter in these ratio estimators. In both parts numerical illustrations and simulation studies were used to analyze the results of the proposed estimators with the comparative estimators to achieve the main results of the research. The intention of this research is to analyze, introduce and improve robust estimation methodologies to solve real life problems such as outliers in data. And finally in 5th chapter conclusion is written based upon whole the results of research.
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    Some New Insights of Exponential Weighted Moving Average Control Chart with Fuzzy Control Lenses
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Muhammad Javed; SP16-RST-001; LHR TP 5249; Dr. Tajammal Hussain
    Statistical process control (SPC) is an approaching path that analyzes processes despite the fact whether they are under statistical control or out of control. So, Control charts are commonly employed for this purpose. Possibly, sample data may possess some uncertainties occurring owing to systems of measurement and conditions of the surrounding environment, for this purpose fuzzy numbers or language variables are usable to seize such form of uncertainties. Thereby, the well-known control charts, exponentially weighted moving average control chart (EWMA) to univariate data are formed up under the environment that is fuzzy one. The fuzzy EWMA control charts (FEWMA) are also usable in order to detect minute changes in the data presented by fuzzy numbers. FEWMA lessens various mistaken and unreliable decisions by catering further flexibleness upon the control limits. As far as the production process is monitored and controlled by dint of FEWMA control chart.
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    A Four-Parameter Generalized Burr Distribution and Its Applications
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Sadia Sarfraz Khan; SP17-RST-002; LHR TP 5325; Dr Muhammad Mohsin
    We study a new distribution called “A Four Parameter Generalized Burr Distribution” and present a detailed account of some of its structural properties including the r-th moment, moments about mean, skewness, kurtosis, and order statistics. The parameters of the under study distribution are estimated by the maximum likelihood method. The simulation study is also carried out under for different sample sizes to test the effectiveness of this model. Finally, the performance of the model is checked by using the real data set and comparing it with other known distributions.
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    Development of Fuzzy X ̅-S Control Charts through incorporating Fuzziness in Conventional X ̅-S Control Charts
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Nabila Ali; SP17-RST-006; LHR TP 5324; Dr. Tajammal Hussain
    The mission to progress the quality and productivity of products and services is spreading across the industry and services sectors. Statistical process control (SPC) is a methodology to monitor an ongoing process and to identify unusual patterns and to indicate the need for correction. The most common SPC tools to be used are control charts. Control charts indicate whether or not the process is stable and resulting quality improvement. Control charts help to distinguish between special and common causes of variation which may occur during manufacturing process and influence the quality of a product.
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    Revisiting the Process Capability Indices (PCIs) for the Uni-variate Variable Control Charts by Incorporating the Concept of Fuzziness
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Muhammad Usman Aslam; FA17-RST-001; LHR TP 5742; Dr. Tajammal Hussain
    With the passage of time and global development in the industrial world, the competition for the quality improvement of the manufactured product has also increased. To achieve the quality perfection goal the best utilized technique is quality control. The technique of quality control is merged with the statistical methods to obtain a reliable source for updating and improving the product’s quality and services, it also helps to maintain the actual quality standard. This combination of quality control with the statistical methods is called Statistical Quality Control (SQC). The concept of Statistical Quality Control (SQC) was introduced in 1924 by Dr. Shewhart who is well known as the father of quality control analysis. The principle objective of SQC is to focus on the quality of any organization and to achieve the optimum quality of production as well as services by using the sufficient statistical techniques. According to (Montgomery, 2009) any products quality depends upon its fittness for use and it is judged by the appropriate quality characteristics.
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    Induction of Measurement Error in Acceptance Sampling Plans
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Sajid Ali Naqvi; SP17-RST-004; LHR TP 5741; Dr. Muhammad Noor-ul-Amin
    Measurement error practically exists to a certain degree and part and parcel of measurement process for quantitative observations. Measurement error cause contamination and result in ineffectiveness of the scale sampling plan. In present study the effect of measurement error is investigated for EWMA based scale sampling plan using a linear covariate method when population standard deviation is known or unknown. The same effect is also examined in the presence of auxiliary information when measurement of quality characteristic is not feasible or accessible. Multiple measurements method is a remedy to minimize the effect of measurement error. The variance of measurement error component becomes zero for infinite number of multiple measurements. A reasonable and economical number of multiple measurements may restore the effectiveness and utilization of EWMA based scale sampling plans with or without auxiliary information when population standard deviation is known or unknown. Tables are provided for various values of model parameters for industrial use
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