29 citations · 55 across the 21 of their papers we have counts for
21 papers
Can Generalized Extreme Value Model Fit the Real Stocks
Sen Lin, Ao Kong, Robert Azencott
The Generalized Extreme Value (GEV) distribution plays a critical role in risk assessment across various domains, such as hydrology, climate science, and finance. In this study, we…
Multi-Quantile Estimators for the parameters of Generalized Extreme Value distribution
Sen Lin, Ao Kong, Robert Azencott
We introduce and study Multi-Quantile estimators for the parameters of Generalized Extreme Value (GEV) distributions to provide a robust approach to extreme value mode…
Rare Events Analysis and Computation for Stochastic Evolution of Bacterial Populations
Yingxue Su, Brett Geiger, Ilya Timofeyev +2
In this paper, we develop a computational approach for computing most likely trajectories describing rare events that correspond to the emergence of non-dominant genotypes. This wo…
An Operator-Splitting Approach for Variational Optimal Control Formulations for Diffeomorphic Shape Matching
Andreas Mang, Jiwen He, Robert Azencott
We present formulations and numerical algorithms for solving diffeomorphic shape matching problems. We formulate shape matching as a variational problem governed by a dynamical sys…
Automatic classification of deformable shapes
Hossein Dabirian, Radmir Sultamuratov, James Herring +4
Let be a dataset of smooth 3D-surfaces, partitioned into disjoint classes , . We show how optimized diffeomorphic registration applied…
Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies
Sorena Sarmadi, James J. Winkle, Razan N. Alnahhas +4
Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deforma…
Diffeomorphic Shape Matching by Operator Splitting in 3D Cardiology Imaging
Peng Zhang, Andreas Mang, Jiwen He +3
We develop an operator splitting approach to solve diffeomorphic matching problems for sequences of surfaces in three-dimensional space. The goal is to smoothly match, at a very fa…
Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis
Ao Kong, Robert Azencott, Hongliang Zhu +1
Studying the micro-trading behaviors before stock price jumps is an important problem for financial regulations and investment decisions. In this study, we provide a new framework…
Predicting intraday jumps in stock prices using liquidity measures and technical indicators
Ao Kong, Hongliang Zhu, Robert Azencott
Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, rele…
Localization of Epileptic Seizure Focus by Computerized Analysis of fMRI Recordings
Rasoul Hekmati, Robert Azencott, Wei Zhang +2
By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eigh…
Fitness Estimation for Genetic Evolution of Bacterial Populations
Sergey S. Sarkisov, Ilya Timofeyev, Robert Azencott
In this paper we develop and test algorithmic techniques to estimate genotypes fitnesses by analysis of observed daily frequency data monitoring the long-term evolution of bacteria…
Large Deviations Analysis for Stochastic Models of Bacterial Evolution
Robert Azencott, Brett Geiger, Ilya Timofeyev
Radical shifts in the genetic composition of large cell populations are rare events with quite low probabilities that direct numerical simulations generally fail to evaluate accura…
Realized volatility and parametric estimation of Heston SDEs
Robert Azencott, Peng Ren, Ilya Timofeyev
We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns and the squared volatilities $V…
Large deviations for Gaussian diffusions with delay
Robert Azencott, Brett Geiger, William Ott
Dynamical systems driven by nonlinear delay SDEs with small noise can exhibit important rare events on long timescales. When there is no delay, classical large deviations theory qu…
Region-of-Interest reconstruction from truncated cone-beam projections
Robert Azencott, Bernhard G. Bodmann, Tasadduk Chowdhury +3
Region-of-Interest (ROI) tomography aims at reconstructing a region of interest inside a body using only x-ray projections intersecting with the goal to reduce overall radi…
Parametric Estimation from Approximate Data: Non-Gaussian Diffusions
Robert Azencott, Peng Ren, Ilya Timofeyev
We study the problem of parameters estimation in Indirect Observability contexts, where is an unobservable stationary process parametrized by a vector of unknown para…
Markov Random Fields and Mass Spectra Discrimination
Ao Kong, Robert Azencott
For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learnings…
Option Pricing Accuracy for Estimated Heston Models
Robert Azencott, Yutheeka Gadhyan, Roland Glowinski
We consider assets for which price and squared volatility are jointly driven by Heston joint stochastic differential equations (SDEs). When the parameters of these SDEs…
Accuracy of Maximum Likelihood Parameter Estimators for Heston volatility SDE
Robert Azencott, Yutheeka Gadhyan
We study approximate maximum likelihood estimators (MLEs) for the parameters of the widely used Heston stock and volatility stochastic differential equations (SDEs). We compute exp…
Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations
Chinmaya Gupta, José Manuel López, Robert Azencott +3
Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from t…
Realtime market microstructure analysis: online Transaction Cost Analysis
Robert Azencott, Arjun Beri, Yutheeka Gadhyan +3
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discov…