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Majnu John

Publications and source records attributed to Majnu John.

10 recordsLinked to original sources
arXiv2026

Accounting for overdispersion and clustering in binomial data from N-of-1 trials

N-of-1 trials are patient centered randomized controlled trials. Although the primary goal of N-of-1 trials is to obtain the results for each patient separately, pooling the results across patients also has relevance. In this paper, we present two analytical strategies to pool the results across N-of-1 trials, when the main outcome for each patient is a binomial variable. Our first method takes into account the extra-binomial variation, while as the second approach takes into account hierarchical clustering in addition to overdispersion. We illustrate the methods using real data analysis and compare the methods using simulations.

stat.AP↗
arXiv2025

Arbitrage-Free Pricing with Diffusion-Dependent Jumps

Standard jump-diffusion models assume independence between jumps and diffusion components. We develop a multi-type jump-diffusion model where jump occurrence and magnitude depend on contemporaneous diffusion movements. Unlike previous one-sided models that create arbitrage opportunities, our framework includes upward and downward jumps triggered by both large upward and large downward diffusion increments. We derive the explicit no-arbitrage condition linking the physical drift to model parameters and market risk premia by constructing an Equivalent Martingale Measure using Girsanov's theorem and a normalized Esscher transform. This condition provides a rigorous foundation for arbitrage-free pricing in models with diffusion-dependent jumps.

q-fin.MF↗
arXiv2022

A novel nonconvex, smooth-at-origin penalty for statistical learning

Nonconvex penalties are utilized for regularization in high-dimensional statistical learning algorithms primarily because they yield unbiased or nearly unbiased estimators for the parameters in the model. Nonconvex penalties existing in the literature such as SCAD, MCP, Laplace and arctan have a singularity at origin which makes them useful also for variable selection. However, in several high-dimensional frameworks such as deep learning, variable selection is less of a concern. In this paper, we present a nonconvex penalty which is smooth at origin. The paper includes asymptotic results for ordinary least squares estimators regularized with the new penalty function, showing asymptotic bias that vanishes exponentially fast. We also conducted an empirical study employing deep neural network architecture on three datasets and convolutional neural network on four datasets. The empirical study showed better performance for the new regularization approach in five out of the seven datasets.

stat.ML↗
arXiv2021

Weak-consistent dynamic correlation estimators for Brownian motion pairs and for Geometric Brownian motion pairs

Estimating dynamic correlation between a pair of time series is of importance in many applications. We present new estimators for the dynamic correlation between a pair of correlated Brownian motions and separately for dynamic correlation between a pair of correlated Geometric Brownian motions. We show that, as the sample size increases, all estimators presented in this paper converge in probability to the underlying true dynamic correlation.

math.PR↗
arXiv2020

Regularized deep learning with nonconvex penalties

Regularization methods are often employed in deep learning neural networks (DNNs) to prevent overfitting. For penalty based DNN regularization methods, convex penalties are typically considered because of their optimization guarantees. Recent theoretical work have shown that nonconvex penalties that satisfy certain regularity conditions are also guaranteed to perform well with standard optimization algorithms. In this paper, we examine new and currently existing nonconvex penalties for DNN regularization. We provide theoretical justifications for the new penalties and also assess the performance of all penalties with DNN analyses of seven datasets.

stat.ML↗
arXiv2017

Nonparametrically estimating dynamic bivariate correlation using visibility graph algorithm

Dynamic conditional correlation (DCC) is a method that estimates the correlation between two time series across time. Although used primarily in finance so far, DCC has been proposed recently as a model-based estimation method for quantifying functional connectivity during fMRI experiments. DCC could also be used to estimate the dynamic correlation between other types of time series such as local field potentials (LFP's) or spike trains recorded from distinct brain areas. DCC has very nice properties compared to other existing methods, but its applications for neuroscience are currently limited because of non-optimal performance in the presence of outliers. To address this issue, we developed a robust nonparametric version of DCC, based on an adaptation of the weighted visibility graph algorithm which converts a time series into a weighted graph. The modified DCC demonstrated better performance in the analysis of empirical data sets: one fMRI data set collected from a human subject performing a Stroop task; and one LFP data set recorded from an awake rat in resting state. Nonparametric DCC has the potential of enlarging the spectrum of analytical tools designed to assess the dynamic coupling and uncoupling of activity among brain areas.

stat.AP↗
arXiv2017

Adaptation of the visibility graph algorithm to find the time lag between hydrogeological time series

Estimating the time lag between two hydrogeologic time series (e.g. precipitation and water levels in an aquifer) is of significance for a hydrogeologist-modeler. In this paper, we present a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. We present simulation results to assess the performance of the method. We also illustrate the utility of our approach using a real world hydrogeologic dataset.

stat.AP↗
arXiv2017

Confidence Intervals for Finite Difference Solutions

Although applications of Bayesian analysis for numerical quadrature problems have been considered before, it's only very recently that statisticians have focused on the connections between statistics and numerical analysis of differential equations. In line with this very recent trend, we show how certain commonly used finite difference schemes for numerical solutions of ordinary and partial differential equations can be considered in a regression setting. Focusing on this regression framework, we apply a simple Bayesian strategy to obtain confidence intervals for the finite difference solutions. We apply this framework on several examples to show how the confidence intervals are related to truncation error and illustrate the utility of the confidence intervals for the examples considered.

stat.CO↗
arXiv2016

A simulations approach for meta-analysis of genetic association studies based on additive genetic model

Genetic association studies are becoming an important component of medical research. To cite one instance, pharmacogenomics which is gaining prominence as a useful tool for personalized medicine is heavily reliant on results from genetic association studies. Meta-analysis of genetic association studies is being increasingly used to assess phenotypic differences between genotype groups. When the underlying genetic model is assumed to be dominant or recessive, assessing the phenotype differences based on summary statistics, reported for individual studies in a meta-analysis, is a valid strategy. However, when the genetic model is additive, a similar strategy based on summary statistics will lead to biased results. This fact about the additive model is one of the things that we establish in this paper, using simulations. The main goal of this paper is to present an alternate strategy for the additive model based on simulating data for the individual studies. We show that the alternate strategy is far superior to the strategy based on summary statistics.

stat.AP↗