Signal Detection and Inference Based on the Beta Binomial Autoregressive Moving Average Model

Autor:	Palm, B. G., Bayer, F. M., Cintra, R. J.
Rok vydání:	2022
Předmět:	Statistics - Methodology Electrical Engineering and Systems Science - Signal Processing Mathematics - Statistics Theory Physics - Data Analysis Statistics and Probability Statistics - Applications
Zdroj:	Digital Signal Processing, Volume 109, February 2021, 102911
Druh dokumentu:	Working Paper
DOI:	10.1016/j.dsp.2020.102911
Popis:	This paper proposes the beta binomial autoregressive moving average model (BBARMA) for modeling quantized amplitude data and bounded count data. The BBARMA model estimates the conditional mean of a beta binomial distributed variable observed over the time by a dynamic structure including: (i) autoregressive and moving average terms; (ii) a set of regressors; and (iii) a link function. Besides introducing the new model, we develop parameter estimation, detection tools, an out-of-signal forecasting scheme, and diagnostic measures. In particular, we provide closed-form expressions for the conditional score vector and the conditional information matrix. The proposed model was submitted to extensive Monte Carlo simulations in order to evaluate the performance of the conditional maximum likelihood estimators and of the proposed detector. The derived detector outperforms the usual ARMA- and Gaussian-based detectors for sinusoidal signal detection. We also presented an experiment for modeling and forecasting the monthly number of rainy days in Recife, Brazil. Comment: 17 pages, 4 tables, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.00095 Zobrazit plný text záznamu View this record from Arxiv