Touqeer Ahmad

• Hasan, Taha & Ahmad, Touqeer (2026). Order-of-addition in mixture and component-amount designs in orthogonal blocks. Journal of Biopharmaceutical Statistics. ISSN 1054-3406. doi: 10.1080/10543406.2026.2680927. Fulltekst i vitenarkiv Vis sammendrag

  Mixture experiments often involve process variables, where orthogonal block designs allow independent estimation of mixture component parameters and process variable parameters. Traditionally, the focus in mixture experiments has been on the proportions of components rather than the order in which they are added. This paper introduces the concept of order-of- addition (OofA) effects in mixture blends within orthogonal blocks, where the response depends on both the proportions of the components and their sequence of addition. We also extend this framework to mixture-amount experiments in orthogonal blocks, incorporating OofA effects into component-amount designs. We first construct mixture and component-amount designs in non-orthogonal blocks, including pairwise ordering variables. To improve efficiency, we then employ the Threshold Accepting (TA) algorithm to reduce the number of runs and generate G-optimal orthogonal designs in two blocks for both mixture and component-amount experiments. In accordance, the proposed designs allow estimation of primary mixture or component-amount parameters, as well as their interactions with OofA effects, without confounding with block effects. The efficiency of the methodology is demonstrated through illustrative examples from pharmaceutical applications using simulated responses. The findings highlight the potential of orthogonally blocked OofA designs to provide a more comprehensive understanding of mixture and component-amount systems in biomedical and pharmaceutical research.

• Hussain, Abid & Ahmad, Touqeer (2026). A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data. Journal of Statistical Computation and Simulation. ISSN 0094-9655. 96(13), s. 3252–3272. doi: 10.1080/00949655.2026.2660176. Fulltekst i vitenarkiv Vis sammendrag

  A fundamental challenge in comparing two survival distributions with right-censored data is the selection of an appropriate nonparametric test, as the power of standard tests like the Log-rank and Wilcoxon is highly dependent on the often-unknown nature of the alternative hypothesis. This paper introduces a new, distribution-free two-sample test designed to overcome this limitation. The proposed method is based on a strategic decomposition of the data into uncensored and censored subsets, from which a composite test statistic is constructed as the sum of two independent Wilcoxon statistics. This design allows the test to automatically and inherently adapt to various patterns of difference – including early, late, and crossing hazards – without requiring pre-specified parameters, pre-testing, or complex weighting schemes. An extensive Monte Carlo simulation study demonstrates that the proposed test robustly maintains the nominal Type I error rate. Crucially, its power is highly competitive with the optimal traditional tests in standard scenarios and superior in complex settings with crossing survival curves, while also exhibiting remarkable robustness to high levels of censoring. The test’s power effectively approximates the maximum power achievable by either the Log-rank or Wilcoxon tests across a wide range of alternatives, offering a powerful, versatile, and computationally simple tool for survival analysis.

• Abbas, Aamar; Ahmad, Touqeer & Ahmad, Ishfaq (2026). Regional modeling of zero-inflated precipitation in Pakistan by classical and Bayesian approaches. Journal of Hydrology Regional Studies. 65. doi: 10.1016/j.ejrh.2026.103330. Fulltekst i vitenarkiv Vis sammendrag

  Study region: Pakistan exhibits pronounced spatial and seasonal variability in rainfall patterns, with extreme precipitation posing major challenges for flood risk management, agriculture, and infrastructure planning. Accurate estimation of return levels remains difficult due to sampling variability, threshold selection, and the presence of zero-inflation associated with dry days. The present study analyzes daily precipitation data from 135 districts across Pakistan for the period 2001–2023, encompassing diverse climatic changes. Study focus: A zero-inflated Extended Generalized Pareto Distribution (ziEGPD) model is introduced to characterize the full precipitation spectrum, including dry days, within a regional modeling framework. Within the regional model setting, homogeneous regions based on uppertail behavior were constructed using the ̂𝛥 ratio method. To capture spatial variation in the region, the parameters of ziEGPD were modeled as functions of covariates using Generalized Additive Models (GAMs). The developed model was estimated through maximum-likelihood and Bayesian frameworks and evaluated via cross-validation using accuracy and robustness measures. New hydrological insights: Results indicate that the Bayesian GAM–ziEGPD model with covariates provides the highest accuracy and consistently outperforms the MLE-based approach across all seasons and clusters. This model is therefore adopted as the optimal framework for estimating regional quantiles across a range of return periods. Regional analysis further shows that the monsoon period exhibits the highest 100-year return levels, with intense rainfall concentrated in southern and southeastern Pakistan.

• Ahmad, Touqeer & Arshad, Irshad Ahmad (2026). New flexible versions of the extended generalized Pareto distribution for count data. Journal of Applied Statistics. ISSN 0266-4763. doi: 10.1080/02664763.2026.2634785. Fulltekst i vitenarkiv Vis sammendrag

  Accurate modeling is essential in integer-valued real phenomena, including the distribution of entire data, zero-inflated (ZI) data, and discrete exceedances. The Poisson and Negative Binomial distributions, along with their ZI variants, are considered suitable for modeling the entire data distribution, but they fail to capture the heavy tail behavior effectively alongside the bulk of the distribution. In contrast, the discrete generalized Pareto distribution (DGPD) is preferred for high threshold exceedances, but it becomes less effective for low threshold exceedances. However, in some applications, the selection of a suitable high threshold is challenging, and the asymptotic conditions required for using DGPD are not always met. To address these limitations, extended versions of DGPD are proposed. These extensions are designed to modeloneofthreescenarios: first, the entire distribution of the data, including both bulk and tail and bypassing the threshold selection step; second, the entire distributionalongwithZI;andthird,thetailofthedistributionforlowthresholdexceedances.Theproposedextensionsofferimprovedestimates across all three scenarios compared to existing models, providing moreaccurateandreliableresults insimulation studies andrealdata applications.

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