Results 71 to 80 of about 211 (152)
On estimation of parameters for spatial autoregressive model
Autoregressive models, Estimation of parameters, Random fields, Primary: 62M10, 62M40, Secondary: 91B72,
Vygantas Paulauskas, Youri Davydov
core +1 more source
On inference for threshold autoregressive models
Bayesian model comparison, reversible jump, threshold autoregression, Wolfe’s sunspot, 62M10, 62F15,
Yu-Jau Lin, Osnat Stramer
core +1 more source
Dynamic modeling of mean-reverting spreads for statistical arbitrage
Mean reversion, Statistical arbitrage, Pairs trading, State space model, Time-varying autoregressive processes, Dynamic regression, Bayesian forecasting, 91B84, 91B28, 62M10,
K. Triantafyllopoulos, G. Montana
core +1 more source
Testing interaction in some predator–prey populations
Correlated Ornstein–Uhlenbeck process, Ergodic and stationary process, Gaussian process, Wiener process, Uniformly most powerful test, Interaction parameter, Lotka–Volterra ODE, 62M10, 62F03, 92D25,
Sévérien Nkurunziza
core +1 more source
An overview of bootstrap methods for estimating and predicting in time series
Autoregressive processes, blockwise bootstrap, moving average processes, moving blocks bootstrap, resampling methods, stationary bootstrap, Primary 62G09, secondary 62G07, 62M10, 62M20, 60G25,
Ricardo Cao
core +1 more source
Diagnostic checking for non-stationary ARMA models with an application to financial data
This paper first derives the limiting distributions of the residual and the squared residual autocorrelation functions of the nonstationary autoregressive moving-average model, respectively.
Zhu, Ke, Ling, Shiqing, Yee, Chong Ching
core +3 more sources
A Matrix-Variate t Model for Networks. [PDF]
Front Artif Intell, 2021 Billio M +3 more
europepmc +1 more source
MONTE CARLO AND NUMERICAL METHODS TO SOLVE THE TIME SERIES MODEL
In this paper we will solve the nonlinear system of equations in the parameters of the time series model by Monte Carlo methods and by numerical methods. When we identify the variance and the inter-covariances of time series, we obtain, dividing by
CIUIU, Daniel
core +1 more source
A statistical framework for measuring the temporal stability of human mobility patterns. [PDF]
J Appl Stat, 2021 Dong Z, Chen YC, Dobra A.
europepmc +1 more source
Detección de raíces unitarias y cointegración mediante métodos de subespacios
, 2005 Clasificación AMS: 62M10 - 62H20En este trabajo se propone un nuevo procedimiento para detectar raíces unitarias basado en métodos de subespacios. Nuestra propuesta tiene tres aspectos originales principales. Primero, la misma metodología puede aplicarse
Casals Carro, José +2 more
core +1 more source