Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases

We investigate the optimality for model selection of the so-called slope heuristics, $V$-fold cross-validation and $V$-fold penalization in a heteroscedastic with random design regression context. We consider a new class of linear models that we call strongly localized bases and that generalize histograms, piecewise polynomials and compactly supported wavelets. We derive sharp oracle inequalities that prove the asymptotic optimality of the slope heuristics---when the optimal penalty shape is known---and $V$ -fold penalization. Furthermore, $V$-fold cross-validation seems to be suboptimal for a fixed value of $V$ since it recovers asymptotically the oracle learned from a sample size equal to $1-V^{-1}$ of the original amount of data. Our results are based on genuine concentration inequalities for the true and empirical excess risks that are of independent interest. We show in our experiments the good behavior of the slope heuristics for the selection of linear wavelet models. Furthermore, $V$-fold cross-validation and $V$-fold penalization have comparable efficiency

Exploring the Role of Managerial Ability in Determining Firm Efficiency

This paper explores the role of management ability in explaining efficiency on New York dairy farms. Using an unbalanced panel of farm data from 1993 through 2004, we estimate input and output-oriented technical efficiencies, cost efficiencies and revenue efficiencies using stochastic frontier functions. We include various input variables as efficiency effects and find lagged net farm income is a preferred measure of management ability over farmers' own estimates of the value of their labor and management. We also find increasing efficiency with operator education, farm size, and extended participation in a farm management program and decreasing efficiency with operator age.Management and Efficiency, Stochastic Frontier Analysis, Productivity Analysis,