15 research outputs found
Conformal Predictive Distributions with Kernels
This paper reviews the checkered history of predictive distributions in
statistics and discusses two developments, one from recent literature and the
other new. The first development is bringing predictive distributions into
machine learning, whose early development was so deeply influenced by two
remarkable groups at the Institute of Automation and Remote Control. The second
development is combining predictive distributions with kernel methods, which
were originated by one of those groups, including Emmanuel Braverman.Comment: 20 pages, 3 figures, prepared for the Proceedings of the Braverman
Readings (Boston, 28-30 April 2017
A Nonparametric Test of The Non-Convexity of Regression.
This paper proposes a nonparametric regression test of non-convexity of a smooth regression function based on least-squares or hybrid splines. By a simple formulation of the convexity hypothesis in the class of all polynomial cubic splines, we build a test which has asymptotically size alpha and is asymptotically unbiased and consistent.ECONOMETRICS
Functional Estimation Under Shape Constraints.
econometrics ; economic models
GeoXp: an R package for exploratory spatial data analysis
We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use data bases coming from the spdep package to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves, etc. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map
Analysing Ambulatory Blood Pressure Monitoring Data with Multivariate Sliced Inverse Regression.
This paper proposes to use a semi parametric regression method, named Sliced Inverse Regression (SIR hereafter), to analyse ambulatory blood pressure monitoring data.ECONOMETRICS;EVALUATION;HEALTH;MATHEMATICS