Conjugate Priors Represent Strong Pre-Experimental Assumptions

Abstract.  It is well known that Jeffreys' prior is asymptotically least favorable under the entropy risk, i.e. it asymptotically maximizes the mutual information between the sample and the parameter. However, in this paper we show that the prior that minimizes (subject to certain constraints) the mutual information between the sample and the parameter is natural conjugate when the model belongs to a natural exponential family. A conjugate prior can thus be regarded as maximally informative in the sense that it minimizes the weight of the observations on inferences about the parameter; in other words, the expected relative entropy between prior and posterior is minimized when a conjugate prior is used.     

Military conscription and university enrolment: evidence from Italy

This paper analyses the effect of the abolition of compulsory military service (CMS) on university enrolment in Italy. A triple-difference model is used to account for various potentially confounding factors. The identification strategy exploits variation along three dimensions: (a) between gender, (b) between age-groups and (c) between time periods. The results show that there is no statistically significant evidence suggesting that the abolition of CMS has a causal effect on the decision to enrol in university studies. Estimates obtained employing a double-difference model exploiting variation along the dimensions (a) and (b) overestimate the effect. Finally, although there is no statistically significant overall effect, we find some evidence of heterogeneous effects between students from different social backgrounds.     

Sensory analysis via multi-block multivariate additive PLS splines

In the last decade, much effort has been spent on modelling dependence between sensory variables and chemical–physical ones, especially when observed at different occasions/spaces/times or if collected from several groups (blocks) of variables. In this paper, we propose a nonlinear generalization of multi-block partial least squares with the inclusion of variable interactions. We show the performance of the method on a known data set.     

Influence of urbanization on the avian species-area relationship: insights from the breeding birds of Rome

Urban Ecosystems - The species-area relationship (SAR) is one of the most investigated patterns in ecology and conservation biology, yet there is no study testing how different levels of...     

Multilevel dimensionality-reduction methods

When data sets are multilevel (group nesting or repeated measures), different sources of variations must be identified. In the framework of unsupervised analyses, multilevel simultaneous component analysis (MSCA) has recently been proposed as the most satisfactory option for analyzing multilevel data. MSCA estimates submodels for the different levels in data and thereby separates the “within”-subject and “between”-subject variations in the variables. Following the principles of MSCA and the strategy of decomposing the available data matrix into orthogonal blocks, and taking into account the between- and the within data structures, we generalize, in a multilevel perspective, multivariate models in which a matrix of response variables can be used to guide the projections (formed by responses predicted by explanatory variables or by a limited number of their combinations/composites) into choices of meaningful directions. To this end, the current paper proposes the multilevel version of the multivariate regression model and dimensionality-reduction methods (used to predict responses with fewer linear composites of explanatory variables). The principle findings of the study are that the minimization of the loss functions related to multivariate regression, principal-component regression, reduced-rank regression, and canonical-correlation regression are equivalent to the separate minimization of the sum of two separate loss functions corresponding to the between and within structures, under some constraints. The paper closes with a case study of an application focusing on the relationships between mental health severity and the intensity of care in the Lombardy region mental health system.     

Response surface methodology for non-equispaced experimental configurations applied to a discrete and stochastic simulator of a manufacturing company

This work is based on the construction and use of a SLAM II PC-based simulator for a medium-sized company manufacturing machine tools designed to work glass. Response surface methods and several experimental design techniques were applied to this company to optimize simulator performances. Since there is a feedback loop on the actual manufacturing system, the theory of orthogonal polynomials was applied to the case-study relative to a planned experimental configuration of non-equispaced tests performed on the real system, which were based on analysis characteristics. This discussion is the ideal extension of the basic case involving equispaced configurations. A summary is presented of the results obtained from the application of a 3X4 mixed level factorial experiment implemented with a discrete and stochastic industrial simulator.     

EQUILIBRIUM SEARCH UNEMPLOYMENT,ENDOGENOUS PARTICIPATION,AND LABOR MARKET FLOWS

The sustainability of welfare states requires high employment and high participation to raise the tax base. To analyze labor supply in a world with market frictions, we propose and solve a macro model of the labor market with unemployment and labor force participation as endogenous and distinct states. In our world, workers' decisions of participating are determined by an entry decision and an exit decision. A calibration of the model improves the usual representations of labor markets, since it quantitatively accounts for the observed flows between employment and nonparticipation. The paper investigates also the effect of payroll taxes and unemployment benefits on participation decisions. Taxes reduce entries and increase exits, whereas unemployment benefits, at a given job‐finding rate, raise entries and have ambiguous effects on exits. (JEL: J2, J6)     

A bayesian predictive approach to sequential search for an optimal dose: Parametric and nonparametric models

Summary This paper looks at a new approach to the problem of finding the maximal tolerated dose (or optimal dose, Eichhorn and Zacks, 1973) of certain drugs which in addition to their therapeutic effects have secondary harmful effects. The problem is investigated in a sequential setting from a Bayesian predictive approach. Search procedures are proposed for parametric and nonparametric models.     

Inequality indices and the starshaped principle of transfers

The evaluation of income distributions is usually based on the Pigou-Dalton (PD) principle which says that a transfer from any people to people who have less decreases economic inequality, i.e., increases the social evaluation index. We introduce two weaker principles of transfers which refer to a parameter θ. With the new principles, only those PD transfers increase the social evaluation index which take from the class of incomes above θ and give to the class below θ. The relative positions of individuals remain unchanged, and either no individual may cross the line θ (principle of transfers about θ) or some may do who have been situated next to it (starshaped principle of transfers at θ). θ may be a given constant, a function of mean income, or a quantile of the income distribution. The classes of indices which are consistent with these transfers are completely characterized, and examples are given.