# Detection and Statistical Modeling of Birth-Death Anomaly

arXiv: https://arxiv.org/abs/1906.11788 · HTML: https://mlsanity.com/paper/1906.11788/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Human Age Estimation from Gene Expression Data using Artificial Neural Networks](https://mlsanity.com/paper/2111.02692/) (2021)
- [Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air](https://mlsanity.com/paper/2104.03226/) (2021)
- [An Information-Theoretic Framework for Identifying Age-Related Genes Using Human Dermal Fibroblast Transcriptome Data](https://mlsanity.com/paper/2111.02595/) (2021)

## Abstract (arXiv)

Generally, anomaly detection has a great importance particularly in applied statistical signal processing. Here we provide a general framework in order to detect anomaly through the statistical modeling. In this paper, it is assumed that a signal is corrupted by noise whose variance follows an ARMA model. The assumption on the signal is further compromised to encompass the inherent nonstationarity associated with natural phenomenon, hence, the signal of interest is assumed to follow an ARIMA model and the noise to denote an anomaly, however, unknown. Anomaly is assumed to possess heteroskedastic properties, therefore, ARCH/GARCH modeling could extract the anomaly pattern given an additive model for signal of interest and anomaly.