Results 21 to 30 of about 198 (153)
Volatility filtering in estimation of kurtosis (and variance)
Dependence Modeling, 2019 The kurtosis of the distribution of financial returns characterized by high volatility persistence and thick tails is notoriously difficult to estimate precisely.
Anatolyev Stanislav
doaj +1 more source
Sample correlations of infinite variance time series models: an empirical and theoretical study
International Journal of Stochastic Analysis, Volume 11, Issue 3, Page 255-282, 1998., 1998 When the elements of a stationary ergodic time series have finite variance the sample correlation function converges (with probability 1) to the theoretical correlation function. What happens in the case where the variance is infinite? In certain cases, the sample correlation function converges in probability to a constant, but not always.
Jason Cohen +2 more
wiley +1 more source
Prediction of time series by statistical learning: general losses and fast rates
Dependence Modeling, 2013 We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the ...
Alquier Pierre +2 more
doaj +1 more source
Peramalan curah hujan di Provinsi Aceh menggunakan metode Box-Jenkins
Majalah Ilmiah Matematika dan Statistika, 2023 Floods are one of the natural disasters that frequently occur in Indonesia, including in Aceh Province. Floods primarily occur when rainfall is intense, mainly in the rainy season.
Nurhafifah Nurhafifah +5 more
doaj +1 more source
The empirical TES methodology: modeling empirical time series
International Journal of Stochastic Analysis, Volume 10, Issue 4, Page 333-353, 1997., 1997 TES (Transform‐Expand‐Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo‐1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms ‐ monotone, oscillatory ...
Benjamin Melamed
wiley +1 more source
Introducing model uncertainty by moving blocks bootstrap [PDF]
, 2006 62M10, 62F40, sieve bootstrap, klockwise bootstrap, prediction, time series, model uncertainty,
Daniel Peña +7 more
core +1 more source
Bootstrap tests for nonparametric comparison of regression curves with dependent errors [PDF]
, 2007 Hypothesis testing, Regression models, Nonparametric estimators, Dependent data, 62G08, 62G09, 62G10, 62M10,
W. González-Manteiga +3 more
core +1 more source
Exponential inequalities for nonstationary Markov chains
Dependence Modeling, 2019 Exponential inequalities are main tools in machine learning theory. To prove exponential inequalities for non i.i.d random variables allows to extend many learning techniques to these variables.
Alquier Pierre +2 more
doaj +1 more source
Geoestadística aplicada a series de tiempo autorregresivas: un estudio de simulación
Revista Integración, 2017 La geoestadística puede usarse como método de predicción de datos faltantes en series temporales. El procedimiento se basa en el estudio de la estructura de autocorrelación temporal de la serie de tiempo por medio de la función de variograma, que es ...
Ramón Giraldo +2 more
doaj +1 more source
Ingeniería y Ciencia, 2015 En este trabajo se identificó un modelo en series de tiempo para el control de la tasa de penetración (ROP) en un pozo de referencia denominado V∗∗∗ que pertenece al campo en desarrollo VEL que está ubicado en la cuenca del Valle del Magdalena Medio (VMM)
Henry Daniel Hernández Martínez +1 more
doaj +1 more source