Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis. The theoretical analysis motivates us to introduce a new multi-class classification machine based on $\ell_p$-norm regularization, where the parameter $p$ controls the complexity of the corresponding bounds. We derive an efficient optimization algorithm based on Fenchel duality theory. Benchmarks on several real-world datasets show that the proposed algorithm can achieve significant accuracy gains over the state of the art

Within US Trade and the Long Shadow of the American Secession

Using data from the US commodity flow surveys, we show that the historical Union-Confederacy border lowers contemporaneous trade between US states by about 16 percentrelative to trade flows within the former alliances. Amongst one million placebos, thereis no other constellation of state grouping that would yield a larger border effect. Thefinding is robust over different econometric models, treatment of the rest of the world,available survey waves, or levels of aggregation. Including contemporaneous controls,such as network, institutional or demographic variables, and Heckscher-Ohlin or Linderterms, lowers the estimate only slightly. Historical variables, such as the incidence ofslavery, do not explain the effect away. Adding US states unaffected by the Civil War,we argue that the friction is not merely reflecting unmeasured North-South differences.Finally, the estimated border effect is larger for differentiated than for homogeneousgoods, stressing the potential role for cultural factors and trust.American Secession, border effect, intranational trade, gravity, US state levels

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers%u2019 heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available.

Analysis of tissue surrounding thyroid nodules by ultrasound digital images

Since US is not easily reproducible, the digital image analysis (IA) has been proposed so that the image evaluation is not subjective. In fact, IA meets the criteria of objectivity, accurateness, and reproducibility by a matrix of pixels whose value is displayed in a gray level. This study aims at evaluating via IA the tissue surrounding a thyroid nodule (backyard tissue, BT) from goitres with benign (b-BT) and malignant (m-BT) lesions. Sixty-nine US images of thyroid nodules surrounded by adequate thyroid tissue was classified as normoechoic and homogeneous were enrolled as study group. Forty-three US images from normal thyroid (NT) glands were included as controls. Digital images of 800 × 652 pixels were acquired at a resolution of eight bits with a 256 gray levels depth. By one-way ANOVA, the 43 NT glands were not statistically different (P = 0.91). Mean gray level of normal glands was significantly higher than b-BT (P = 0.026), and m-BT (P = 0.0001), while no difference was found between b-BT and m-BT (P = 0.321). NT tissue boundary external to the nodule was found at 6.0 ± 0.5 mm in cancers and 4.0 ± 0.5 mm in benignancies (P = 0.001). These data should indicate that the tissue surrounding a thyroid nodule may be damaged even when assessed as normal by US. This is of interest to investigate the extranodular effects of thyroid tumors

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers' heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available.Technology and Industry

A two-stage genome-wide association study of sporadic amyotrophic lateral sclerosis

The cause of sporadic amyotrophic lateral sclerosis (ALS) is largely unknown, but genetic factors are thought to play a significant role in determining susceptibility to motor neuron degeneration. To identify genetic variants altering risk of ALS, we undertook a two-stage genome-wide association study (GWAS): we followed our initial GWAS of 545 066 SNPs in 553 individuals with ALS and 2338 controls by testing the 7600 most associated SNPs from the first stage in three independent cohorts consisting of 2160 cases and 3008 controls. None of the SNPs selected for replication exceeded the Bonferroni threshold for significance. The two most significantly associated SNPs, rs2708909 and rs2708851 [odds ratio (OR) = 1.17 and 1.18, and P-values = 6.98 x 10–7 and 1.16 x 10–6], were located on chromosome 7p13.3 within a 175 kb linkage disequilibrium block containing the SUNC1, HUS1 and C7orf57 genes. These associations did not achieve genome-wide significance in the original cohort and failed to replicate in an additional independent cohort of 989 US cases and 327 controls (OR = 1.18 and 1.19, P-values = 0.08 and 0.06, respectively). Thus, we chose to cautiously interpret our data as hypothesis-generating requiring additional confirmation, especially as all previously reported loci for ALS have failed to replicate successfully. Indeed, the three loci (FGGY, ITPR2 and DPP6) identified in previous GWAS of sporadic ALS were not significantly associated with disease in our study. Our findings suggest that ALS is more genetically and clinically heterogeneous than previously recognized. Genotype data from our study have been made available online to facilitate such future endeavors

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution