arXiv subjects

Touqeer Ahmad

Publications and source records attributed to Touqeer Ahmad.

18 recordsLinked to original sources

arXiv2026

A spatial duration-augmented framework for drought persistence

Drought monitoring across heterogeneous climate regions requires explicit modeling of drought persistence rather than relying solely on index classification. We develop a Bayesian spatial, duration-augmented framework for drought persistence that captures both temporal memory and spatial dependence. Because persistence estimates depend on the underlying drought index, we also assess the influence of (i) reference evapotranspiration (RET) formulations and (ii) the choice of probability distribution used to standardize the climatic water balance. The framework is applied to a high-resolution (0.25$^\circ \times$ 0.25$^\circ$) dataset over 730 grid cells across Italy. The choice of RET formulation (Penman-Monteith, Hargraves, Thornthwaite) has only a marginal influence on the overall drought signal: spatially averaged SPEI series at the 3-, 6-, and 12-month scales are identical across methods. By contrast, the proposed bulk-and-tails (BATs) distribution substantially improves index estimation, consistently outperforming commonly used alternative models across all RET methods and timescales and achieving normality acceptance rates of 98.4%--99.9% compared to 37%--92% for the generalized extreme value (GEV) distribution and 0%--30% for others. The spatial duration-augmented model further outperforms a nonspatial counterpart, producing spatially continuous, uncertainty-aware recovery and survival fields. Results reveal a strong south-north gradient in temporal memory: southern peninsular and island regions (Sicily, Sardinia) exhibit pronounced persistence and duration-dependent recovery hazards, whereas northern and Alpine regions show near-zero duration dependence. Drought characterization is more sensitive to the distributional and persistence assumptions than to the RET formulation, and duration-insensitive classifications systematically understate extended drought risk in southern Italy.

stat.AP↗

arXiv2026

Logistic lasso regression with nearest neighbors for gradient-based dimension reduction

This paper investigates a new approach to estimate the gradient of the conditional probability given the covariates in the binary classification framework. The proposed approach consists of fitting a localized nearest-neighbor logistic model with $\ell_1$-penalty in order to cope with possibly high-dimensional covariates. Our theoretical analysis shows that the pointwise convergence rate of the gradient estimator is optimal under very mild assumptions. Moreover, using an outer product of such gradient estimates at several points in the covariate space, we provide a new method for estimating the central subspace, a well-known object allowing to carry out dimension reduction within the covariate space. Our implementation uses cross-validation on the misclassification rate to estimate the dimension of this subspace. We find that the proposed approach outperforms existing competitors in synthetic and real data applications.

math.ST↗

arXiv2025

Flexible model for varying levels of zeros and outliers in count data

Count regression models are necessary for examining discrete dependent variables alongside covariates. Nonetheless, when data display outliers, overdispersion, and an abundance of zeros, traditional methods like the zero-inflated negative binomial (ZINB) model sometimes do not yield a satisfactory fit, especially in the tail regions. This research presents a versatile, heavy-tailed discrete model as a resilient substitute for the ZINB model. The suggested framework is built by extending the generalized Pareto distribution and its zero-inflated version to the discrete domain. This formulation efficiently addresses both overdispersion and zero inflation, providing increased flexibility for heavy-tailed count data. Through intensive simulation studies and real-world implementations, the proposed models are thoroughly tested to see how well they work. The results show that our models always do better than classic negative binomial and zero-inflated negative binomial regressions when it comes to goodness-of-fit. This is especially true for datasets with a lot of zeros and outliers. These results highlight the proposed framework's potential as a strong and flexible option for modeling complicated count data.

stat.ME↗

arXiv2025

Order of Addition in Mixture-Amount Experiments

In a mixture experiment, we study the behavior and properties of $m$ mixture components, where the primary focus is on the proportions of the components that make up the mixture rather than the total amount. Mixture-amount experiments are specialized types of mixture experiments where both the proportions of the components in the mixture and the total amount of the mixture are of interest. In this paper, we consider an Order-of-Addition (OofA) mixture-amount experiment in which the response depends on both the mixture amounts of components and their order of addition. Full mixture OofA designs are constructed to maintain orthogonality between the mixture-amount model terms and the effects of the order of addition. \answer{But the number of runs in such full OofA designs increases as $m$ increases. We employ the Threshold Accepting (TA) Algorithm to select an n-row subset from the full Order-of-Addition (OofA) mixture design that maximizes G-optimality while minimizing the number of experimental runs. Further, the G-efficiency criterion is used to assess how well the design supports the precise and unbiased estimation of the model parameters.} These designs enable the estimation of mixture-component model parameters and the order-of-addition effects. The Fraction of Design Space (FDS) plot is used to provide a visual assessment of the prediction capabilities of a design across the entire design space.

stat.ME↗

arXiv2025

Concentration and excess risk bounds for imbalanced classification with synthetic oversampling

Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approach in practice, its theoretical foundations remain underexplored. We develop a theoretical framework to analyze the behavior of SMOTE and related methods when classifiers are trained on synthetic data. We first derive a uniform concentration bound on the discrepancy between the empirical risk over synthetic minority samples and the population risk on the true minority distribution. We then provide a nonparametric excess risk guarantee for kernel-based classifiers trained using such synthetic data. These results lead to practical guidelines for better parameter tuning of both SMOTE and the downstream learning algorithm. Numerical experiments are provided to illustrate and support the theoretical findings

stat.ML↗

arXiv2025

New flexible versions of extended generalized Pareto model for count data

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 model one of three scenarios: first, the entire distribution of the data, including both bulk and tail and bypassing the threshold selection step; second, the entire distribution along with ZI; and third, the tail of the distribution for low threshold exceedances. The proposed extensions offer improved estimates across all three scenarios compared to existing models, providing more accurate and reliable results in simulation studies and real data applications.

stat.ME↗

arXiv2025

A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data

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 Mann Whitney 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 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.

stat.ME↗

arXiv2025

Modeling zero-inflated precipitation extremes

Accurate modeling of daily rainfall, encompassing both dry and wet days as well as extreme precipitation events, is critical for robust hydrological and climatological analyses. This study proposes a zero-inflated extended generalized Pareto distribution model that unifies the modeling of dry days, low, moderate, and extreme rainfall within a single framework. Unlike traditional approaches that rely on prespecified threshold selection to identify extremes, our proposed model captures tail behavior intrinsically through a tail index that aligns with the generalized Pareto distribution. The model also accommodates covariate effects via generalized additive modeling, allowing for the representation of complex climatic variability. The current implementation is limited to a univariate setting, modeling daily rainfall independently of covariates. Model estimation is carried out using both maximum likelihood and Bayesian approaches. Simulation studies and empirical applications demonstrate the models flexibility in capturing zero inflation and heavy-tailed behavior characteristics of daily rainfall distributions.

stat.AP↗

arXiv2025

Space time modeling for drought classification and prediction

The Standardized Precipitation Index (SPI) is a critical tool for monitoring drought conditions, typically relying on normalized accumulated precipitation. While longer historical records of precipitation yield more accurate parameter estimates of marginal distribution, they often reflect nonstationary influences such as anthropogenic climate change and multidecadal natural variability. Traditional approaches either overlook this nonstationarity or address it with quasi-stationary reference periods. This study introduces a novel nonstationary SPI framework that utilizes generalized additive models (GAMs) to flexibly model the spatiotemporal variability inherent in drought processes. GAMs are employed to estimate parameters of the Gamma distribution, while dual extreme tails flexible models are integrated to robustly capture the probabilistic risk of extreme drought events. Future drought and wet extremes events in terms of return levels are calculated using an extended generalized Pareto distribution, which offers flexibility in modeling the entire distribution of the data while bypassing the threshold selection step. Results demonstrate that the proposed nonstationary SPI model is both stable and capable of reproducing known nonstationary drought patterns, while also providing new insights into the evolving dynamics of drought. This approach represents a significant advancement in drought modeling under changing climatic conditions.

stat.AP↗

arXiv2024

Order of Addition in Orthogonally Blocked Mixture and Component-Amount Designs

Mixture experiments often involve process variables, such as different chemical reactors in a laboratory or varying mixing speeds in a production line. Organizing the runs in orthogonal blocks allows the mixture model to be fitted independently of the process effects, ensuring clearer insights into the role of each mixture component. Current literature on mixture designs in orthogonal blocks ignores the order of addition of mixture components in mixture blends. This paper considers the order of addition of components in mixture and mixture-amount experiments, using the variable total amount taken into orthogonal blocks. The response depends on both the mixture proportions or the amounts of the components and the order of their addition. Mixture designs in orthogonal blocks are constructed to enable the estimation of mixture or component-amount model parameters and the order-of-addition effects. The G-efficiency criterion is used to assess how well the design supports precise and unbiased estimation of the model parameters. The fraction of the Design Space plot is used to provide a visual assessment of the prediction capabilities of a design across the entire design space.

stat.ME↗

arXiv2024

Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches

This study examines the interdependence between cryptocurrencies and international financial indices, such as MSCI World and MSCI Emerging Markets. We compute the value at risk, expected shortfall (ES), and range value at risk (RVaR) and investigate the dynamics of risk spillover. We employ a hybrid approach to derive these risk measures that integrate GARCH models, extreme value models, and copula functions. This framework uses a bivariate portfolio approach involving cryptocurrency data and traditional financial indices. To estimate the above risks of these portfolio structures, we employ symmetric and asymmetric GARCH and both tail flexible EVT models as marginal to model the marginal distribution of each return series and apply different copula functions to connect the pairs of marginal distributions into a multivariate distribution. The empirical findings indicate that the eGARCH EVT-based copula model adeptly captures intricate dependencies, surpassing conventional methodologies like Historical simulations and t-distributed parametric in VaR estimation. At the same time, the HS method proves superior for ES, and the t-distributed parametric method outperforms RVaR. Eventually, the Diebold-Yilmaz approach will be applied to compute risk spillovers between four sets of asset sequences. This phenomenon implies that cryptocurrencies reveal substantial spillover effects among themselves but minimal impact on other assets. From this, it can be concluded that cryptocurrencies propose diversification benefits and do not provide hedging advantages within an investor's portfolio. Our results underline RVaR superiority over ES regarding regulatory arbitrage and model misspecification. The conclusions of this study will benefit investors and financial market professionals who aspire to comprehend digital currencies as a novel asset class and attain perspicuity in regulatory arbitrage.

q-fin.RM↗

arXiv2024

An extended generalized Pareto regression model for count data

The statistical modeling of discrete extremes has received less attention than their continuous counterparts in the Extreme Value Theory (EVT) literature. One approach to the transition from continuous to discrete extremes is the modeling of threshold exceedances of integer random variables by the discrete version of the generalized Pareto distribution. However, the optimal choice of thresholds defining exceedances remains a problematic issue. Moreover, in a regression framework, the treatment of the majority of non-extreme data below the selected threshold is either ignored or separated from the extremes. To tackle these issues, we expand on the concept of employing a smooth transition between the bulk and the upper tail of the distribution. In the case of zero inflation, we also develop models with an additional parameter. To incorporate possible predictors, we relate the parameters to additive smoothed predictors via an appropriate link, as in the generalized additive model (GAM) framework. A penalized maximum likelihood estimation procedure is implemented. We illustrate our modeling proposal with a real dataset of avalanche activity in the French Alps. With the advantage of bypassing the threshold selection step, our results indicate that the proposed models are more flexible and robust than competing models, such as the negative binomial distribution

stat.ME↗

arXiv2022

Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-training the network at different resolutions. However re-training a network is rarely cheap and not always viable. To this end, we propose a simple solution to address the train-test distributional shift and enhance the performance of pre-trained models -- which commonly ship as a package with deep learning platforms \eg, PyTorch. Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off. Instead we propose to combine results for multiple random crops for a test image. This not only matches the train time augmentation but also provides the full coverage of the input image. We explore combining representation of random crops through averaging at different levels \ie, deep feature level, logit level, and softmax level. We demonstrate that, for various families of modern deep networks, such averaging results in better validation accuracy compared to using a single central crop per image. The softmax averaging results in the best performance for various pre-trained networks without requiring any re-training or fine-tuning whatsoever. On modern GPUs with batch processing, the paper's approach to inference of pre-trained networks, is essentially free as all images in a batch can all be processed at once.

cs.CV↗

arXiv2022

A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data. Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning. This paper formalizes various open-world learning problems including open-world learning without labels. These open-world problems can be addressed with modifications to known elements, we present a new framework that enables agents to combine various modules for novelty-detection, novelty-characterization, incremental learning, and instance management to learn new classes from a stream of unlabeled data in an unsupervised manner, survey how to adapt a few state-of-the-art techniques to fit the framework and use them to define seven baselines for performance on the open-world learning without labels problem. We then discuss open-world learning quality and analyze how that can improve instance management. We also discuss some of the general ambiguity issues that occur in open-world learning without labels.

cs.CV↗

arXiv2021

Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented reality applications. We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions. Unlike earlier approaches, which either rely on extraction of explicit feature descriptors and their classification, or fine-tuning general scene parsing deep networks for sky segmentation, our approach learns linear filters based on local structure analysis. At test time, for every candidate edge pixel, a single filter is chosen from the set of learned filters based on pixel's structure tensor, and then applied to the patch around it. We then employ dynamic programming to solve the shortest path problem for the resultant multistage graph to get the sky-mountain boundary. The proposed approach is computationally faster than earlier methods while providing comparable performance and is more suitable for resource constrained platforms e.g., mobile devices, planetary rovers and UAVs. We compare our proposed approach against earlier skyline detection methods using four different data sets. Our code is available at \url{https://github.com/TouqeerAhmad/skyline_detection}.

cs.CV↗

arXiv2021

Self-Supervised Features Improve Open-World Learning

This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}. We accomplish this by proposing a comprehensive generalize-able open-world learning protocol capable of evaluating various components of open-world learning in an operational setting. We argue that in true open-world learning, the underlying feature representation should be learned in a self-supervised manner. Under this self-supervised feature representation, we introduce the problem of detecting unknowns as samples belonging to Out-of-Label space. We differentiate between Out-of-Label space detection and the conventional Out-of-Distribution detection depending upon whether the unknowns being detected belong to the native-world (same as feature representation) or a new-world, respectively. Our unifying open-world learning framework combines three individual research dimensions, which typically have been explored independently, i.e., Incremental Learning, Out-of-Distribution detection and Open-World Learning. Starting from a self-supervised feature space, an open-world learner has the ability to adapt and specialize its feature space to the classes in each incremental phase and hence perform better without incurring any significant overhead, as demonstrated by our experimental results. The incremental learning component of our pipeline provides the new state-of-the-art on established ImageNet-100 protocol. We also demonstrate the adaptability of our approach by showing how it can work as a plug-in with any of the self-supervised feature representation methods.

cs.CV↗

arXiv2020

Automatic Open-World Reliability Assessment

Image classification in the open-world must handle out-of-distribution (OOD) images. Systems should ideally reject OOD images, or they will map atop of known classes and reduce reliability. Using open-set classifiers that can reject OOD inputs can help. However, optimal accuracy of open-set classifiers depend on the frequency of OOD data. Thus, for either standard or open-set classifiers, it is important to be able to determine when the world changes and increasing OOD inputs will result in reduced system reliability. However, during operations, we cannot directly assess accuracy as there are no labels. Thus, the reliability assessment of these classifiers must be done by human operators, made more complex because networks are not 100% accurate, so some failures are to be expected. To automate this process, herein, we formalize the open-world recognition reliability problem and propose multiple automatic reliability assessment policies to address this new problem using only the distribution of reported scores/probability data. The distributional algorithms can be applied to both classic classifiers with SoftMax as well as the open-world Extreme Value Machine (EVM) to provide automated reliability assessment. We show that all of the new algorithms significantly outperform detection using the mean of SoftMax.

cs.CV↗

arXiv2018

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on \textbf{user-in-the-loop} skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection\cite{Ahmad15}, second focused on visual geo-localization but relying on accurate detection of skyline \cite{Saurer16} and other two proposed for general semantic segmentation -- Fully Convolutional Networks (FCN) \cite{Long15} and SegNet\cite{Badrinarayanan15}. Each of the first two methods is trained on a common training set \cite{Baatz12} comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.

cs.CV↗