Estimating Learning Models with Experimental Data

We study the statistical properties of three estimation methods for a model of learning that is often tted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identi cation issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties are obtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated

Asteroid Models from Multiple Data Sources

In the past decade, hundreds of asteroid shape models have been derived using the lightcurve inversion method. At the same time, a new framework of 3-D shape modeling based on the combined analysis of widely different data sources such as optical lightcurves, disk-resolved images, stellar occultation timings, mid-infrared thermal radiometry, optical interferometry, and radar delay-Doppler data, has been developed. This multi-data approach allows the determination of most of the physical and surface properties of asteroids in a single, coherent inversion, with spectacular results. We review the main results of asteroid lightcurve inversion and also recent advances in multi-data modeling. We show that models based on remote sensing data were confirmed by spacecraft encounters with asteroids, and we discuss how the multiplication of highly detailed 3-D models will help to refine our general knowledge of the asteroid population. The physical and surface properties of asteroids, i.e., their spin, 3-D shape, density, thermal inertia, surface roughness, are among the least known of all asteroid properties. Apart for the albedo and diameter, we have access to the whole picture for only a few hundreds of asteroids. These quantities are nevertheless very important to understand as they affect the non-gravitational Yarkovsky effect responsible for meteorite delivery to Earth, or the bulk composition and internal structure of asteroids.Comment: chapter that will appear in a Space Science Series book Asteroids I

Future supernovae data and quintessence models

The possibility to unambiguously determine the equation-of-state of the cosmic dark energy with existing and future supernovae data is investigated. We consider four evolution laws for this equation-of-state corresponding to four quintessential models, i.e. i) a cosmological constant, ii) a general barotropic fluid, iii) a perfect fluid with a linear equation-of-state and iv) a more physical model based on a pseudo-Nambu-Goldstone boson field. We explicitly show the degeneracies present not only within each model but also between the different models : they are caused by the multi-integral relation between the equation-of-state of dark energy and the luminosity distance. Present supernova observations are analysed using a standard $\chi^2$ method and the minimal $\chi^2$ values obtained for each model are compared. We confirm the difficulty to discriminate between these models using present SNeIa data only. By means of simulations, we then show that future SNAP observations will not remove all the degeneracies. For example, wrong estimations of $\Omega_m$ with a good value of $\chi^2_{min}$ could be found if the right cosmological model is not used to fit the data. We finally give some probabilities to obtain unambiguous results, free from degeneracies. In particular, the probability to confuse a cosmological constant with a true barotropic fluid with an equation-of-state different from -1 is shown to be 95% at a $2 \sigma$ level.Comment: 12 pages. This improved version has been accepted for publication in M.N.R.A.

Wealth inequality: data and models

In the United States wealth is highly concentrated and very unequally distributed: the richest 1% hold one third of the total wealth in the economy. Understanding the determinants of wealth inequality is a challenge for many economic models. We summarize some key facts about the wealth distribution and what economic models have been able to explain so far.Wealth