Differenced estimators of variance bypass the estimation of regression function and thus are simple to calculate. However, there exist two problems: most differenced estimators do not achieve the asymptotic optimal rate for the mean square error; for finite samples the estimation bias is also important and not further considered. In this paper, we estimate the variance as the intercept in a lin...

This study assesses two alternative approaches for investigating linear and nonlinear lagged associations in environmental time series data, comparing through simulations simple methods based on moving average summaries with more flexible distributed lag linear and nonlinear models. Results indicate that the latter provide estimates with no or low bias and close-to-nominal confidence intervals,...

In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

Statistical analysis of high-dimensional functional times series arises in various applications. Under this scenario, addition to the intrinsic infinite-dimensionality data, number variables can grow with serially dependent observations. In paper, we focus on theoretical relevant estimated cross-(auto)covariance terms between two multivariate time or a mixture and scalar beyond Gaussianity assu...

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applicatio...

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time varying parameters consistently track actual interest rate movements better than...

Kopijyvä Kuopio 2004 Finland Kolehmainen, Mikko T. Data exploration with self-organizing maps in environmental informatics and bioinformatics. ABSTRACT The aim of this thesis was to evaluate the usability of self-organizing maps and some other methods of computational intelligence in analysing and modelling problems of environmental informatics and bioinformatics. The concepts of environmental ...