Computational statistics (Master's level course), Spring 2023, Spring 2021 (42%), Spring 2022 (42%)

Experimental design (Master's level course), Fall 2021 (50%)

Statistical method (Master's level course), Spring 2020 (33%)

Multivariate method (Undergraduate level), Spring 2020, Fall 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022 (57%)

Econometrics (Undergraduate level), Spring 2022

 

A selection from Stockholm University publication database

• Fitting probability distributions to economic growth

  2016. Mahmood Ul Hassan, Pär Stockhammar. Journal of Applied Statistics 43 (9), 1583-1603

  Article

  The growth rate of the gross domestic product (GDP) usually carries heteroscedasticity, asymmetry and fat-tails. In this study three important and significantly heteroscedastic GDP series are examined. A Normal, normal-mixture, normal-asymmetric Laplace distribution and a Student's t-Asymmetric Laplace (TAL) distribution mixture are considered for distributional fit comparison of GDP growth series after removing heteroscedasticity. The parameters of the distributions have been estimated using maximum likelihood method. Based on the results of different accuracy measures, goodness-of-fit tests and plots, we find out that in the case of asymmetric, heteroscedastic and highly leptokurtic data the TAL-distribution fits better than the alternatives. In the case of asymmetric, heteroscedastic but less leptokurtic data the NM fit is superior. Furthermore, a simulation study has been carried out to obtain standard errors for the estimated parameters. The results of this study might be used in e.g. density forecasting of GDP growth series or to compare different economies.

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• Optimal Item Calibration for Computerized Achievement Tests

  2019. Mahmood Ul Hassan, Frank Miller. Psychometrika 84 (4), 1101-1128

  Article

  Item calibration is a technique to estimate characteristics of questions (called items) for achievement tests. In computerized tests, item calibration is an important tool for maintaining, updating and developing new items for an item bank. To efficiently sample examinees with specific ability levels for this calibration, we use optimal design theory assuming that the probability to answer correctly follows an item response model. Locally optimal unrestricted designs have usually a few design points for ability. In practice, it is hard to sample examinees from a population with these specific ability levels due to unavailability or limited availability of examinees. To counter this problem, we use the concept of optimal restricted designs and show that this concept naturally fits to item calibration. We prove an equivalence theorem needed to verify optimality of a design. Locally optimal restricted designs provide intervals of ability levels for optimal calibration of an item. When assuming a two-parameter logistic model, several scenarios with D-optimal restricted designs are presented for calibration of a single item and simultaneous calibration of several items. These scenarios show that the naive way to sample examinees around unrestricted design points is not optimal.

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• An exchange algorithm for optimal calibration of items in computerized achievement tests

  2021. Mahmood Ul Hassan, Frank Miller. Computational Statistics & Data Analysis 157

  Article

  The importance of large scale achievement tests, like national tests in school, eligibility tests for university, or international assessments for evaluation of students, is increasing. Pretesting of questions for the above mentioned tests is done to determine characteristic properties of the questions by adding them to an ordinary achievement test. If computerized tests are used, it has been shown using optimal experimental design methods that it is efficient to assign pretest questions to examinees based on their abilities. The specific distribution of abilities of the available examinees are considered and restricted optimal designs are applied. A new algorithm is developed which builds on an equivalence theorem. It discretizes the design space with the possibility to change the grid adaptively during the run, makes use of an exchange idea and filters computed designs. It is illustrated how the algorithm works through some examples as well as how convergence can be checked. The new algorithm is flexible and can be used even if different models are assumed for different questions.

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• Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

  2021. Mahmood Ul Hassan, Zahra Noreen, Rashid Ahmed. International Journal of Climatology 41 (8), 4307-4320

  Article

  Extreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L‐moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L‐moment ratio diagram and ZDist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K‐fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others.

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Show all publications by Mahmood Ul Hassan at Stockholm University