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Una clase de estimadores para los parámetros de un proceso AR(1), obtenidos a partir de estimaciones no paramétricas previas.

Wenceslao Gonzalez Manteiga, Juan Manuel Vilar Fernández (1987)

Trabajos de Estadística

Sea {Xt}t ∈ Z+ una serie de tiempo estacionaria que sigue el modelo autorregresivo de orden 1: Xt = λ + ρXt-1 + et, siendo {et} variables aleatorias i.i.d. de media cero y varianza σ2; a partir de una muestra del proceso {X1, ..., Xn} se calcula en una primera etapa τ'n, estimador no paramétrico de la función de predicción τ(x) = E[Xt/Xt-1 = x] y Ω'n, estimador no paramétrico de la función de distribución asociada al proceso. Esto nos permite en una segunda etapa calcular estimaciones de los parámetros...

Una familia de distribuciones conjugadas para un proceso ARE (1).

Enrique Caro, Juan Ignacio Domínguez, Francisco Javier Girón (1991)

Trabajos de Estadística

En este artículo se estudia, desde una perspectiva bayesiana, un proceso AR(1) con errores exponenciales, ARE(1): para ello se construye una nueva familia de distribuciones conjugadas, denotada por CDG, que permite construir una especie de filtro de Kalman para la estimación recursiva de los parámetros del modelo.

Uniform deterministic equivalent of additive functionals and non-parametric drift estimation for one-dimensional recurrent diffusions

D. Loukianova, O. Loukianov (2008)

Annales de l'I.H.P. Probabilités et statistiques

Usually the problem of drift estimation for a diffusion process is considered under the hypothesis of ergodicity. It is less often considered under the hypothesis of null-recurrence, simply because there are fewer limit theorems and existing ones do not apply to the whole null-recurrent class. The aim of this paper is to provide some limit theorems for additive functionals and martingales of a general (ergodic or null) recurrent diffusion which would allow us to have a somewhat unified approach...

Unit root test in the presence of a single additive outlier small sample case

Hocine Fellag, Safia Abdouche (2002)

Discussiones Mathematicae Probability and Statistics

The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.

Unit root test in the presence of a single additive outlier small sample case

Hocine Fellag (2001)

Discussiones Mathematicae Probability and Statistics

The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.

Unit root test under innovation outlier contamination small sample case

Lynda Atil, Hocine Fellag, Karima Nouali (2006)

Discussiones Mathematicae Probability and Statistics

The two sided unit root test of a first-order autoregressive model in the presence of an innovation outlier is considered. In this paper, we present three tests; two are usual and one is new. We give formulas computing the size and the power of the three tests when an innovation outlier (IO) occurs at a specified time, say k. Using a comparative study, we show that the new statistic performs better under contamination. A Small sample case is considered only.

Using randomization to improve performance of a variance estimator of strongly dependent errors

Artur Bryk (2012)

Applicationes Mathematicae

We consider a fixed-design regression model with long-range dependent errors which form a moving average or Gaussian process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We estimate the variance of the errors using the Rice estimator. The estimator is shown to exhibit weak (i.e. in probability) consistency. Simulation results confirm this property for moderate and large sample sizes when randomization...

Variances in spectral analysis of membrane noise

Francesco Andrietti, Carla Canegallo (1984)

Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni

Le fluttuazioni di conduttanza di un modello di canale del potassio di una fibra muscolare che segue una cinetica di Hodgkin e Huxley sono state analizzate attraverso l'analisi spettrale indiretta. Sono state confrontate due diverse stime della densità spettrale e le loro rispettive varianze: quella della prima stima considerata è già nota, mentre quella della seconda stima è stata ricavata da noi nelle medesime ipotesi (distribuzione normale). I risultati teorici sono stati confrontati con quelli...

Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes

M. Clausel, F. Roueff, M. S. Taqqu, C. Tudor (2014)

ESAIM: Probability and Statistics

We consider stationary processes with long memory which are non-Gaussian and represented as Hermite polynomials of a Gaussian process. We focus on the corresponding wavelet coefficients and study the asymptotic behavior of the sum of their squares since this sum is often used for estimating the long–memory parameter. We show that the limit is not Gaussian but can be expressed using the non-Gaussian Rosenblatt process defined as a Wiener–Itô integral of order 2. This happens even if the original...

Weakly stationary processes with non–positive autocorrelations

Šárka Došlá, Jiří Anděl (2010)

Kybernetika

We deal with real weakly stationary processes { X t , t ∈ ℤ } with non-positive autocorrelations { r k } , i. e. it is assumed that r k ≤ 0 for all k = 1 , 2 , ⋯ . We show that such processes have some special interesting properties. In particular, it is shown that each such a process can be represented as a linear process. Sufficient conditions under which the resulting process satisfies r k ≤ 0 for all k = 1 , 2 , ⋯ are provided as well.

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