A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Maximum likelihood estimation and a test of fit based on the Anderson-Darling statistic is presented for the case of the power law distribution when the parameters are estimated from a left-censored sample. Expressions for the maximum likelihood estimators and tables of asymptotic percentage points for the A^2 statisti…
This work presents a theoretical and empirical evaluation of Anderson-Darling test when the sample size is limited. The test can be applied in order to backtest the risk factors dynamics in the context of Counterparty Credit Risk modelling. We show the limits of such test when backtesting the distributions of an intere…
Neural network improves normality testing accuracy.
In financial time series there are periods in which the value increases or decreases monotonically. We call those periods elemental trends and study the probability distribution of their duration for the indices DJIA, NASDAQ and IPC. It is found that the trend duration distribution often differs from the one expected u…
The paper fits a seven-parameter GTS distribution to financial data.
SafeML monitors ML systems for safety and security risks.
This paper tests the multivariate normality of node degrees in Erdős-Rényi graphs.
Optimizes cover parameter in Mapper algorithm for better visualization.
Unified framework for various probability distribution distances.
Unified score and distance-based GoF tests for model adequacy.