This essay identifies the empirical facts about lobbying which are generally agreed upon in the literature. It then discusses challenges to empirical research in lobbying and provides examples of empirical methods that can be employed to overcome these challenges—with an emphasis on statistical measurement, identification, and casual inference. The essay then discusses the advantages, disadvant...

The consistency of classification algorithm plays a central role in statistical learning theory. A consistent algorithm guarantees us that taking more samples essentially suffices to roughly reconstruct the unknown distribution. We consider the consistency of ERM scheme over classes of combinations of very simple rules (base classifiers) in multiclass classification. Our approach is, under some...

We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before in the area of property elicitation or in machine learning more broadly. As compared to traditional loss functions that take only a single data point, these multi-observation loss functions can in some cases drasticall...

In the strategic management theory, the structure-based view holds that the structural characteristic of the industry is the main determinant of the performance; while the resource-based view claims that the competitive advantage is driven by the resource and capacity of an enterprise. In this paper, we carry out an empirical research on 945 Chinese listed companies’ performance (ROA) in 18 ind...

The seller of a contingent claim H can always find a self-financing investment strategy that (super)hedges the claim H. When the seller wants to endow an initial capital x less than the one required to get perfect (super)hedging, the shortfall risk minimisation problem arises in a natural way. The aim is to find the strategy that minimises E{`([H(ST )−V x,φ T ])} (shortfall risk), where V x,φ t...

In stochastic convex optimization the goal is to minimize a convex function F (x) . = Ef∼D[f(x)] over a convex set K ⊂ R where D is some unknown distribution and each f(·) in the support of D is convex over K. The optimization is commonly based on i.i.d. samples f, f, . . . , f from D. A standard approach to such problems is empirical risk minimization (ERM) that optimizes FS(x) . = 1 n ∑ i≤n f...

In this short survey, I revisit the role of the proximal point method in large scale optimization. I focus on three recent examples: a proximally guided subgradient method for weakly convex stochastic approximation, the prox-linear algorithm for minimizing compositions of convex functions and smooth maps, and Catalyst generic acceleration for regularized Empirical Risk Minimization.