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Modelling for the Wavelet Coefficients of ARFIMA Processes

Modelling for the Wavelet Coefficients of ARFIMA Processes
February 2013 We consider the model for the discrete nonboundary wavelet coefficients of ARFIMA processes. Although many authors have explained the utility of the wavelet transform for the long dependent processes in semiparametrical literature, there have been a few studies in parametric setting.
openaire   +1 more source

Probabilistic forecasting of under-five mortality in Uganda: implications for monitoring SDG 3.2. [PDF]

Front Public Health
Gaston RT, Ramroop S, Habyarimana F.
europepmc   +1 more source

ARFIMA time series models

, 2014
The thesis deal with long-memory processes which are defined by several ways. The main concern is dedicated to ARFIMA model, to its basic properties and its application.
Vdovičenko, Martin
core  

Persistence in ecological footprint inequality across European Union countries: evidence from unit root tests. [PDF]

BMC Ecol Evol
Yazgan Ş, Açdoyuran B, Beşer NÖ, Radulescu M, Tuta L.   +4 more
europepmc   +1 more source

Subtle to significant: Enhancing heaviness perception with stochastic resonance. [PDF]

J Exp Psychol Hum Percept Perform
Grunkemeyer AA, Kelty-Stephen DG, Likens AD.   +2 more
europepmc   +1 more source

Forecasting the Romanian Unemployment Rate in Time of Health Crisis-A Univariate vs. Multivariate Time Series Approach. [PDF]

Int J Environ Res Public Health, 2021
Davidescu AA, Apostu SA, Marin A.
europepmc   +1 more source

Estimating the Fractionally Integrated Model with a Break in the Differencing Parameter [PDF]

This note examines a new problem in the structural-change literature. A fractionally integrated model is assumed to experience a change in the differencing parameter at an unknown time.
Terence Tai-Leung Chong
core  

Tracking progress towards Sustainable Development Goal 3.2 in Kenya using time series models. [PDF]

BMJ Glob Health
Dlamini WJ, Melesse SF, Mwambi HG.
europepmc   +1 more source

Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis. [PDF]

J Glob Health
Wang Y, Liang Y, Zhang B, Yi S, Zhou P, Lan X, Xue C, Li Y, Li X, Xu C.   +9 more
europepmc   +1 more source

ARFIMA model applied to Malaysian stock market

Communications in Mathematical Biology and Neuroscience, 2022
openaire   +1 more source