Islam, M.Qamarul
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Islam, M. Qamarul
Islam, MQ
Islam, MQ
Job Title
Prof. Dr.
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İktisadi ve İdari Birimler Fakültesi
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Former Staff
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Sustainable Development Goals
1NO POVERTY
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2ZERO HUNGER
1
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
3
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
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10REDUCED INEQUALITIES
3
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Scholarly Output
19
Articles
19
Views / Downloads
1324/295
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
208
Scopus Citation Count
225
Patents
0
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0
WoS Citations per Publication
10.95
Scopus Citations per Publication
11.84
Open Access Source
5
Supervised Theses
0
| Journal | Count |
|---|---|
| Communications in Statistics - Theory and Methods | 4 |
| Economic Research-Ekonomska Istraživanja | 2 |
| Journal of Applied Statistics | 2 |
| International Statistical Review | 1 |
| Journal of Business Economics and Finance | 1 |
Current Page: 1 / 3
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19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Article Multiple linear regression model under nonnormality(Taylor & Francis Inc, 2004) Islam, M. Qamarul; Tiku, Moti L.We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.Article Citation - WoS: 20Citation - Scopus: 22Regression Analysis With a Dtochastic Design Variable(Wiley, 2006) Sazak, HS; Tiku, ML; Islam, MQIn regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.Article Citation - WoS: 1Citation - Scopus: 2Sample Design and Allocation for Random Digit Dialling(Springer, 2005) Ayhan, HO; Islam, MQSample design and sample allocation methods are developed for random digit dialling in household telephone surveys. The proposed method is based on a two-way stratification of telephone numbers. A weighted probability proportional to size sample allocation technique is used, with auxiliary variables about the telephone coverage rates, within local telephone exchanges of each substrata. This makes the sampling design nearly "self-weighting" in residential numbers when the prior information is well assigned. A computer program generates random numbers for the local areas within the existing phone capacities. A simulation study has shown greater sample allocation gain by the weighted probabilities proportional to size measures over other sample allocation methods. The amount of dialling required to obtain the sample is less than for proportional allocation. A decrease is also observed on the gain in sample allocation for some methods through the increasing sample sizes.Article Citation - WoS: 62Citation - Scopus: 64Multiple Linear Regression Model Under Nonnormality(Taylor & Francis inc, 2004) Islam, MQ; Tiku, MLWe consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.Article Citation - WoS: 13Citation - Scopus: 13Multiple Linear Regression Model With Stochastic Design Variables(Taylor & Francis Ltd, 2010) Islam, M. Qamarul; Tiku, Moti L.In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.Article Citation - WoS: 42Citation - Scopus: 47Nonnormal Regression.: Ii.: Symmetric Distributions(Taylor & Francis inc, 2001) Tiku, ML; Islam, MQ; Selçuk, ASSalient features of a family of short-tailed symmetric distributions, introduced recently by Tiku and Vaughan [1], are enunciated. Assuming the error distribution to be one of this family, the methodology of modified likelihood is used to derive MML estimators of parameters in a linear regression model. The estimators are shown to be efficient, and robust to inliers. This paper is essentially the first to achieve robustness to infers. The methodology is extended to long-tailed symmetric distributions and the resulting estimators are shown to be efficient, and robust to outliers. This paper should be read in conjunction with Islam et al. [2] who develop modified likelihood methodology for skew distributions in the context of linear regression.Article Citation - WoS: 4Citation - Scopus: 6Mahalanobis Distance Under Non-Normality(Taylor & Francis Ltd, 2010) Tiku, Moti L.; Islam, M. Qamarul; Qumsiyeh, Sahar B.We give a novel estimator of Mahalanobis distance D2 between two non-normal populations. We show that it is enormously more efficient and robust than the traditional estimator based on least squares estimators. We give a test statistic for testing that D2=0 and study its power and robustness properties.Article Citation - WoS: 4Citation - Scopus: 4Estimation in Multivariate Nonnormal Distributions With Stochastic Variance Function(Elsevier Science Bv, 2014) Islam, M. Qamarul; Qamarul Islam, M.In this paper the problem of estimation of location and scatter of multivariate nonnormal distributions is considered. Estimators are derived under a maximum likelihood setup by expressing the non-linear likelihood equations in the linear form. The resulting estimators are analytical expressions in terms of sample values and, hence, are easily computable and can also be manipulated analytically. These estimators are found to be remarkably more efficient and robust as compared to the least square estimators. They also provide more powerful tests in testing various relevant statistical hypotheses. (C) 2013 Elsevier B.V. All rights reserved.Article Citation - WoS: 27Citation - Scopus: 29Nonnormal Regression. I. Skew Distributions(Taylor & Francis inc, 2001) Islam, MQ; Tiku, ML; Yildirim, FIn a linear regression model of the type y = thetaX + e, it is often assumed that the random error e is normally distributed. In numerous situations, e.g., when y measures life times or reaction times, e typically has a skew distribution. We consider two important families of skew distributions, (a) Weibull with support IR: (0, infinity) on the real line, and (b) generalised logistic with support IR: (-infinity, infinity). Since the maximum likelihood estimators are intractable in these situations, we derive modified likelihood estimators which have explicit algebraic forms and are, therefore, easy to compute. We show that these estimators are remarkably efficient, and robust. We develop hypothesis testing procedures and give a real life example.Article Nonnormal Regression.I. Skew Distributions(2001) Islam, M. Qamarul; L. Tiku, Moti; Yildirim, F.In a linear regression model of the typey¼ Xþe, it is oftenassumed that the random erroreis normally distributed. Innumerous situations, e.g., whenymeasures life times or reac-tion times,etypically has a skew distribution. We considertwo important families of skew distributions, (a) Weibull withsupport IR:ð0,1Þon the real line, and (b) generalised logisticwit hsupport IR:ð 1,1Þ. Since the maximum likelihoodestimators are intractable in these situations, we derivemodified likelihood estimators which have explicit algebraicforms and are, therefore, easy to compute. We show that theseestimators are remarkably efficient, and robust. We develophypothesis testing procedures and give a real life example
