Inference for non-regular families of distributions
A part of my research concerns statistical inference for non-regular families of distributions, which do not satisfy the standard regularity conditions assumed in classical statistical inference. In such models, the support of the distribution may depend on unknown parameters (for example, Uniform(0, θ) or three-parameter gamma models), likelihood functions may be unbounded or non-differentiable, and Fisher information may be infinite or undefined. As a result, classical properties such as the existence, consistency, and asymptotic normality of maximum likelihood estimators may fail. I am interested in developing specialized inferential methods for these settings, with particular emphasis on estimation and model selection.
Bayesian and frequentist methods for reliability and life-testing
Lifetime testing plays a key role in reliability engineering, where the goal is to understand failure mechanisms and predict product lifetimes under limited testing resources. I develop frequentist and Bayesian inferential methods for censored and accelerated life-testing experiments, focusing on realistic experimental constraints. My work integrates decision-theoretic ideas into inference and addresses situations involving limited testing time, budget constraints, or uncertainty considerations, where efficient use of data is essential.
Optimal planning of life-testing experiments
I study the optimal planning of life-testing experiments with the aim of maximizing inferential efficiency or minimizing overall testing cost. This includes the design of censored and accelerated life tests under resource constraints and the close integration of experimental design with downstream inference and decision-making objectives.
Bayesian acceptance sampling and decision-oriented statistics
Another strand of my research focuses on Bayesian acceptance sampling plans in statistical quality control. Using Bayesian decision theory, I design accept and reject rules that balance inspection cost, product quality, and the risks faced by both producers and consumers. These methods are motivated by applications in manufacturing (such as batteries, semiconductors, and medical devices) and pharmaceutical quality control (including drug batch testing), where reliability and regulatory compliance are critical.
Computation and implementation
My research places strong emphasis on computational feasibility. I implement my methods using modern statistical computing tools, including R and Bayesian frameworks such as Stan, and make use of high-performance computing environments when required to scale algorithms to complex models and large simulation studies.
My background enables interdisciplinary collaboration at the interface of statistics, reliability engineering, and actuarial science. I am particularly interested in problems where rigorous statistical theory, computation, uncertainty quantification, and real-world decision-making intersect in industrial and applied settings.