Andrea Pastore

Characterization of rainwater quality from the Venice region network using multiway data analysis

Abstract The paper describes the results of an exploratory analysis of the relations between variables characterizing the quality of atmospheric precipitation in the Veneto, an Italian region with an area of ∼ 18000 km2 and a population of just over 4.5 million. A network of eight sampling stations for monitoring rainfall was set up in 1988, in line with the EMEP statements. The data consisted of 1174 observations over the period February 1989–December 1991. Principal component analysis (PCA) was used in an attempt to describe the structure of relations between the solutes in wet deposition. Some extensions of PCA (interstructure-compromise-intrastructure method) were considered in order to evaluate differences between relation structures as defined by location, season and volume of precipitations.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up.

Biological plausibility and other prior information could help select genome‐wide association (GWA) findings for further follow‐up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts’ opinions and empirical evidence to estimate the relative importance of 15 types of information at the single‐nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two‐round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta‐analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta‐analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

A comparison between parallel algorithms for system parameter estimation in dynamic linear models

When dealing with high-frequency time series, statistical procedures giving reliable estimates of unknown parameters and forecasts in real time are required. This is why recursive estimation methods are usually preferred to maximum-likelihood estimators. In the paper, a recursive estimation algorithm for the system parameter of dynamic linear models is proposed. A comparison with some other algorithms is given via Monte Carlo simulations. Consistency properties of the algorithms are also empirically verified. Copyright

Recursive Estimation of System Parameter in Environmental Time Series Models

Dealing with high-frequency time series, such as environmental ones, raises important inferential and computational problems. Environmental monitoring and forecasting, for instance, require statistical procedures giving reliable estimates of unknown parameters and forecasts in real time. In this paper we consider dynamic linear models as a basic tool for the analysis of such kind of data and propose a recursive estimator for system parameter. A comparison of this estimator with some other estimation methods is provided via Monte Carlo simulations. The estimator we propose is computationally efficient and very easy to implement. Moreover, in our simulation study, it exhibits good asymptotic properties.

A merging algorithm for Gaussian mixture components

In finite mixture model clustering, each component of the fitted mixture is usually associated with a cluster. In other words, each component of the mixture is interpreted as the probability distribution of the variables of interest conditionally on the membership to a given cluster. The Gaussian mixture model (GMM) is very popular in this context for its simplicity and flexibility. It may happen, however, that the components of the fitted model are not well separated. In such a circumstance, the number of clusters is often overestimated and a better clustering could be obtained by joining some subsets of the partition based on the fitted GMM. Some methods for the aggregation of mixture components have been recently proposed in the literature. In this work, we propose a hierarchical aggregation algorithm based on a generalisation of the definition of silhouette-width taking into account the Mahalanobis distances induced by the precison matrices of the components of the fitted GMM. The algorithm chooses the number of groups corresponding to the hierarchy level giving rise to the highest average-silhouette-width. Some simulation experiments and real data applications indicate that its performance is at least as good as the one of other existing methods.

Flexible Dynamic Regression Models for Real-time Forecasting of Air Pollutant Concentration

The application of the dynamic regression model to real-time forecasting of air pollutant concentration points out some problems due to both the high frequency of sampling and the need of many-step-ahead forecasting. Some flexible definitions of the system equation are proposed to solve these problems. The proposed definitions are evaluated by means of an application to the prediction of nitrogen dioxide concentration in Venezia-Mestre.

On the Comparison of Model-Based Clustering Solutions

In this paper we propose a new similarity index, which can be used to compare model-based clustering solutions. We define also an adjusted-for-chance version, although we advise that, whenever feasible, bootstrap replications should be preferred to chance-corrected similarity indices. We describe the properties of the proposed index and of its chance-corrected version. Finally, we present some applications on simulated and real data.

Covariate Error Bias Effects in Dynamic Regression Model Estimation and Improvement in the Prediction by Covariate Local Clusters

We consider a dynamic linear regression model with errors-in-covariate. Neglecting such errors has some undesirable effects on the estimates obtained with the Kalman Filter. We propose a modification of the Kalman Filter where the perturbed covariate is replaced with a suitable function of a local cluster of covariates. Some results of both a simulation experiment and an application are reported.

Automatic Discount Selection for Exponential Family State-Space Models

In a previous paper (Pastore, 2004), a method for selecting the discount parameter in a gaussian state-space model was introduced. The method is based on a sequential optimization of a Bayes factor and is intended for on-line modelling purposes. In this paper, these results are extended to state-space models where the distribution of the observable variable belongs to the exponential family.

System error variance tuning in state-space models

The multivariate dynamic regression model is a particular specification of the dynamic linear model. For this model, we propose a recursive equation for the estimation of the system error variance matrix. The solution can be used when more observation are available at each state of the system. In these cases, the algorithm allows to define a recursive procedure for the estimate of both the state vector (the regression coefficients) and the other hyperparameters of the model. The performances of the proposed method are evaluated by means of Monte Carlo experiments.