Nonparametric Estimation and Symmetry Tests for Conditional Density Functions.

We suggest two new methods for conditional density estimation. The first is based on locally fitting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always produce non-negative estimators. We propose an algorithm suitable for selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density functions. The proposed methods are illustrated by both simulation and application to a real data set.TESTING ; STATISTICAL ANALYSIS ; ESTIMATION OF PARAMETERS

Statistical Methodological Issues in Studies of Air Pollution and Respiratory Disease.

Epidemiological studies have consistently shown short term associations between levels of air pollution and respiratory disease in countries of diverse populations, geographical locations and varying levels of air pollution and climate. The aims of this paper are: (1) to assess the sensitivity of the observed pollution effects to model specification, with particular emphasis on the inclusion of seasonally adjusted covariates; and (2) to study the effect of air pollution on respiratory disease in Melbourne, Australia.Air pollution; Autocorrelation; Generalized additive models; Seasonal adjustment; Respiratory disease

Using R to Teach Econometrics.

R, an open-source programming environment for data analysis and graphics, has in only a decade grown to become a de-facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting-edge statistical methods which are, by R's open-source nature, available immediately. The software is stable,available at no cost, and exists for a number of platforms. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers.Econometrics; Statistical software; Teaching

Unmasking the Theta Method.

The Theta method of forecasting performed particularly well in the M3-competition and is therefore of interest to forecast practitioners. The description of the method given by Assimakopoulos and Nikolopoulos (2000) involves several pages of algebraic manipulation and is difficult to comprehend. We show that the method can be expressed much more simply; furthermore we show that the forecasts obtained are equivalent to simple exponential smoothing with drift.exponential smoothing; forecasting competitions; state space models

Generalized Additive Modelling of Mixed Distribution Markov Models with Application to Melbourne's Rainfall.

We consider modelling time series using a generalized additive model with first- order Markov structure and mixed transition density having a discrete component at zero and a continuous component with positive sample space. Such models have application, for example, in modelling daily occurrence and intensity of rainfall, and in modelling the number and size of insurance claims. We show how these methods extend the usual sinusoidal seasonal assumption in standard chain- dependent models by assuming a general smooth pattern of occurrence and intensity over time. These models can be fitted using standard statistical software. The methods of Grunwald and Jones (1998) can be used to combine these separate occurrence and intensity models into a single model for amount. We use 36 years of rainfall data from Melbourne, Australia, as a vehicle of illustration, and use the models to investigate the effect of the El Nino phenomenon on Melbourne's rainfall.Time Series ; Econometric Models ; Mixed Distribution Markov Models

Mixed Model-Based Hazard Estimation.

We propose a new method for estimation of the hazard function from a set of censored failure time data, with a view to extending the general approach to more complicated models. The approach is based on a mixed model representation of penalized spline hazard estimators. One payoff is the automation of the smoothing parameter choice through restricted maximum likelihood. Another is the option to use standard mixed model software for automatic hazard estimation.Non-parametric regression; Restricted maximum likelihood; Variance component; Survival analysis.

Empirical Information Criteria for Time Series Forecasting Model Selection

In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be used where there is a single time series to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task. We compare the EIC with other model selection criteria including Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms both the AIC and BIC, particularly for longer forecast horizons. We also compare the criteria on simulated data and find that the EIC does better than existing criteria in that case also.Exponential smoothing; forecasting; information criteria; M3 competition; model selection.

A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods.

We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition on the M3-competition.Automatic forecasting, exponential smoothing, prediction intervals, state space models.

New developments in the forecasting of monthly overnight stays in the North Region of Portugal

The Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.publishe

Are some brain injury patients improving more than ohers?

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progress of a disease and in the evolution of individuals subjected to treatment. We investigate the evolution of patients on the basis of medical tests before and during treatment after brain trauma: we want to understand how similar patients can become to healthy participants. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patient subpopulations, dealing with the above challenges. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls