Sample correlations of infinite variance time series models: An empirical and theoretical study.
Schätzen der Kovarianzdichte stationärer Punktprozesse aus Zählungen.
Seasonal time series with missing observations
Popular exponential smoothing methods dealt originally only with equally spaced observations. When time series contains gaps, smoothing constants have to be adjusted. Cipra et al., following Wright’s approach of irregularly spaced observations, have suggested ad hoc modification of smoothing constants for the Holt-Winters smoothing method. In this article the fact that the underlying model of the Holt-Winters method is a certain seasonal ARIMA is used. Minimum mean square error smoothing constants...
Security price modelling by a binomial tree
We consider multidimensional tree-based models of arbitrage-free and path-independent security markets. We assume that no riskless investment exists. Contingent claims pricing and hedging problems in such a market are studied.
Selección de la ventana en suavización tipo núcleo de la parte no paramétrica de un modelo parcialmente lineal con errores autorregresivos.
Supongamos que yi = ζiT β + m(ti) + εi, i = 1, ..., n, donde el vector (p x 1) β y la función m(·) son desconocidos, y los errores εi provienen de un proceso autorregresivo de orden uno (AR(1)) estacionario. Discutimos aquí el problema de la selección del parámetro ventana de un estimador tipo núcleo de la función m(·) basado en un estimador Generalizado de Mínimos Cuadrados de β. Obtenemos la expresión asintótica de una ventana óptima y proponemos un método para estimarla, de modo que dé lugar...
Semi- and nonparametric ARCH processes.
Semiparametric estimation in autoregressive processes with Markov regime. (Estimación semiparamétrica en procesos autorregresivos con régimen de Markov.)
Sensitivity analysis in linear models
In this work, we consider the general linear model or its variants with the ordinary least squares, generalised least squares or restricted least squares estimators of the regression coefficients and variance. We propose a newly unified set of definitions for local sensitivity for both situations, one for the estimators of the regression coefficients, and the other for the estimators of the variance. Based on these definitions, we present the estimators’ sensitivity results.We include brief remarks...
Sequential Estimation of Means of Linear Processes.
Siegel's test for periodic components in multiple time series and its application in engineering practice
Signal processing and sampling method for obtaining time series corresponding to higher order derivatives.
Smoothing and occupation measures of stochastic processes
This is a review paper about some problems of statistical inference for one-parameter stochastic processes, mainly based upon the observation of a convolution of the path with a non-random kernel. Most of the results are known and presented without proofs. The tools are first and second order approximation theorems of the occupation measure of the path, by means of functionals defined on the smoothed paths. Various classes of stochastic processes are considered starting with the Wiener process,...
Smoothing dichotomy in randomized fixed-design regression with strongly dependent errors based on a moving average
We consider a fixed-design regression model with errors which form a Borel measurable function of a long-range dependent moving average process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We show that the Priestley-Chao kernel estimator of the regression fuction exhibits a dichotomous asymptotic behaviour depending on the amount of smoothing employed. Moreover, the resulting estimator is shown to exhibit...
Sobre la interpretación de modelos ARIMA univariantes.
En este trabajo se demuestra cómo describir un modelo ARIMA de series temporales como suma de una tendencia a largo plazo, un componente estacional y un componente transitorio. Esta descomposición se obtiene a partir de la función de predicción del modelo, y su uso permite apreciar aspectos poco estudiados de los modelos ARIMA.
Sobre la robustificación interna del algoritmo de Plackett-Kalman para la estimación recursiva del modelo de regresión lineal.
Este trabajo presenta un procedimiento para hacer robusto el algoritmo recursivo de Plackett-Kalman para el modelo lineal, incorporándole medidas diagnósticas que indiquen la influencia potencial y real de cada nueva observación en los parámetros del modelo. Se describe cómo calcular recursivamente el estadístico D2 de Cook, la distancia de Mahalanobis de cada nueva observación al centro de gravedad de la ya incluidas, y un contraste, basado en los residuos recursivos, de que la nueva observación...
Some applications of time series models to financial data
Some generalizations in a heteroscedastic RCA (1) model
Some limit properties of an approximate least squares estimator in a nonlinear regression model with correlated nonzero mean errors.
Some models for the voltage.
Some New Time Series Results.