Censored EM algorithm for Weibull mixtures: application to arrival times of market orders
In a previous analysis the problem of "zero-inflated" time data (caused by high frequency trading in the electronic order book) was handled by left-truncating the inter-arrival times. We demonstrated, using rigorous statistical methods, that the Weibull distribution describes the corresponding stochastic dynamics for all inter-arrival time differences except in the region near zero. However, since the truncated Weibull distribution was not able to describe the huge "zero-inflated" probability mass in the neighbourhood of zero (making up approximately 50\% of the data for limit orders), it became clear that the entire probability distribution is a mixture distribution of which the Weibull distribution is a significant part. Here we use a censored EM algorithm to analyse data for the difference of the arrival times of market orders, which usually have a much lower percentage of zero inflation, for four selected stocks trading on the London Stock Exchange.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distributi…
Survival AnalysisLearning Mixtures of Gaussians with Censored Data
We study the problem of learning mixtures of Gaussians with censored data. Statistical learning with censored data is a classical problem, with numerous practical applications, however, finite-sample guarantees for even …
WTNN: Weibull-Tailored Neural Networks for survival analysis
The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to exp…
Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network
We propose to integrate weapon system features (such as weapon system manufacturer, deployment time and location, storage time and location, etc.) into a parameterized Cox-Weibull [1] reliability model via a neural netwo…
Density EstimationSurvPFN: Towards Foundation Models for Survival Predictions
Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched. Unlike in standard regression tasks, survi…
Feature Engineering