Efficiency and Economies of Scale of Large Canadian Banks

The authors measure the economies of scale of Canada's six largest banks and their cost-efficiency over time. Using a unique panel data set from 1983 to 2003, they estimate pooled translog cost functions and derive measures of relative efficiency and economies of scale. The disaggregation of the data allows the authors to model Canadian banks as producing multiple outputs, including non-traditional activities. Given the long time span of the data set, they also incorporate technological and regulatory changes in the banks' cost functions, as well as time-varying bank-specific effects. The authors' model leads them to reject constant returns to scale. These findings suggest that there are potential scale benefits in the Canadian banking industry. The authors also find that technological and regulatory changes have had significant positive effects on the banks' cost structure.Financial institutions

Articulated Pose Estimation Using Hierarchical Exemplar-Based Models

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. Specifically, we obtain more expressive spatial models by assuming independence between exemplars at different levels in the hierarchy; we also obtain stronger spatial constraints by inferring the spatial relations between parts at the same level. As our method strikes a good balance between expressiveness and strength of spatial models, it is both effective and generalizable, achieving state-of-the-art results on different benchmarks: Leeds Sports Dataset and CUB-200-2011.Comment: 8 pages, 6 figure

Are Canadian Banks Efficient? A Canada--U.S. Comparison

The authors compare the efficiency of Canada's largest banks with U.S. commercial banks over the past 20 years. Efficiency is measured in three ways. First, the authors study key performance ratios, and find that Canadian banks are as productive as U.S. banks. Second, they investigate whether there are economies of scale in the production functions of Canadian banks and broadly comparable U.S. bank-holding companies (BHCs). They find larger economies of scale for Canadian banks than for the U.S. BHCs, which suggests that Canadian banks are less efficient in terms of scale, and have more to gain in terms of efficiency benefits from becoming larger. Third, the authors measure cost-inefficiency in Canadian banks and in U.S. BHCs relative to the domestic efficient frontier in each country (the domestic best-practice institution). They find that Canadian banks are closer to the domestic efficient frontier than are the U.S. BHCs. Canadian banks have also moved closer to the domestic efficient frontier than have the U.S. BHCs over time. Finally, the authors examine the dispersion in cost-inefficiency found in Canadian banks and attribute some of the dispersion to differences in information and communication technology investment. Comparisons are made with the U.S. BHC experience.Financial institutions

On Graphical Models via Univariate Exponential Family Distributions

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.Comment: Journal of Machine Learning Researc