On robust estimation of the location parameter

Matrix Differential Calculus with Applications in Statistics and Econometrics

Time-series clustering - A decade review

Preface 1. Long run frequencies 2. The chance set-up 3. Support 4. The long run 5. The law of likelihood 6. Statistical tests 7. Theories of testing 8. Random sampling 9. The fiducial argument 10. Estimation 11. Point estimation 12. Bayes' theory 13. The subjective theory.

Logic of Statistical Inference. By Ian Hacking. Pp. ix, 232. 40s. 1965. (Cambridge University Press)

This programming manual accompanies the PcGive Professional 9.0 software, an interactive modelling package for modelling statistical and economic data for forecasting or observing trends.

Object-orientd matrix programming using OX

A non-parametric method to estimate the number of clusters

https://www.ime.usp.br/~patriota/STAMET-D-08-00113-revised-v2.pdf

A heteroscedastic structural errors-in-variables model with equation error

A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

/pdf/functional-clustering-of-time-series-gene-expression-data-by-3iqdp9vhcs.pdf

Functional clustering of time series gene expression data by Granger causality

https://www.ime.usp.br/~patriota/p-value.pdf

A classical measure of evidence for general null hypotheses

We consider the issue of assessing influence of observations in the class of Birnbaum–Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [8] which are confined to Birnbaum–Saunders linear regression models. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are discussed. Additionally, the normal curvatures for studying local influence are derived under some perturbation schemes. We also give an application to a real fatigue data set.

/pdf/influence-diagnostics-in-birnbaum-saunders-nonlinear-4nup72w5md.pdf

Alexandre G. Patriota

Papers

A non-parametric method to estimate the number of clusters

A heteroscedastic structural errors-in-variables model with equation error

Functional clustering of time series gene expression data by Granger causality

A classical measure of evidence for general null hypotheses

Influence diagnostics in Birnbaum–Saunders nonlinear regression models