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The General Poverty Index
asymptotic behavior empirical process hungarian construction poverty indices
2012/9/17
We introduce the General Poverty Index (GPI),which summarizes most of the known and available poverty indices, in the form GPI=δ(A(Qn,n,Z)nB(Q,n)Qn Xj=1 w(µ1n+µ2Qn−µ3j+µ4...
We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Speci cally if the sequence encountered by the learner is described well by a know...
Information-theoretic Dictionary Learning for Image Classification
Dictionary learning information theory mutual Dictionary learning information theory mutual
2012/9/18
We present a two-stage approach for learning dic-tionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, d...
Statistical Inference of Allopolyploid Species Networks in the Presence of Incomplete Lineage Sorting
Allopolyploid hybridization Bayesian phylogenetics network
2012/9/18
Polyploidy is an important speciation mechanism, particularly in land plants. Allopolyploid species are formed after hybridization betweenother-wise intersterile parental species. Recent theoretical p...
Power-law distributions in binned empirical data
power-law distribution heavy-tailed distributions model selec-tion binned data
2012/9/18
Many man-made and natural phenomena, including the intensity of earthquakes, population of cities, and size of international wars, are believed to follow power-law distributions. The accurate identifi...
Rejoinder to "Statistical Modeling of Spatial Extremes"
Rejoinder "Statistical Modeling of Spatial Extremes"
2012/9/17
We are grateful to the discussants for their posi-tive and interesting comments. In an area moving so rapidly it is to be expected that our review overlooks
some work, and all the contributions helpf...
Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet
Discussion "Statistical Modeling of Spatial Extremes" A. C. Davison, S. A. Padoan A. C. Davison, S. A. Padoan and M. Ribatet M. Ribatet
2012/9/17
We congratulate the authors for producing such a helpful and comprehensive overview paper of a ra-pidly developing and important area. The starting point for inference in spatial extreme value prob-le...
Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet
Discussion "Statistical Modeling of Spatial Extremes" A. C. Davison, S. A. Padoan M. Ribatet
2012/9/17
The review paper on spatial extremes by Davison,Padoan and Ribatet is a most welcome contribu-tion. The authors cover quite a lot of ground, mak-ing connections between different approaches while high...
Nonparametric Inference for Max-Stable Dependence
Nonparametric Inference Max-Stable Dependence
2012/9/17
The choice for parametric techniques in the dis-cussion article is motivated by the claim that for multivariate extreme-value distributions, “owing to
the curse of dimensionality, nonparametric estim...
Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet
Discussio "Statistical Modeling of Spatial Extremes" A. C. Davison, S. A. Padoan M. Ribatet
2012/9/17
We congratulate the authors for their overview paper discussing modeling techniques for spatial ex-tremes. There is great interest in spatial extreme
data in the atmospheric science community, as the...
Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
Composite likelihood coordinatewise optimization Ising model minorization–maximization principle NP-dimension asymptotic theory HIV drug resistance database.
2012/9/17
The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging, especially in the presence of high-dimensional param...
The Dependence of Routine Bayesian Model Selection Methods on Irrelevant Alternatives
Bayesian Model Selection Methods Alternatives
2012/9/17
Bayesian methods - either based on Bayes Factors or BIC - are now widely used for model selection. One property that might reasonably be demanded of any model
selection method is that if a modelM1 is...
Inference of time-varying regression models
Information criterion locally stationary processes nonpara-metric hypothesis testings time-varying coefficient models variable selection.
2012/9/17
We consider parameter estimation, hypothesis testing and vari-able selection for partially time-varying coefficient models. Our asymp-totic theory has the useful feature that it can allow dependent, n...
Structured prediction tasks pose a fundamental trade-o between the need for model com-plexity to increase predictive power and the limited computational resources for inference in the exponentially-s...
Distance Metric Learning for Kernel Machines
metric learning distance learning support vector machines semi-de nite programming Mahalanobis distance
2012/9/17
Recent work in metric learning has signi cantly improved the state-of-the-art ink-nearest neighbor classi cation. Support vector machines (SVM), particularly with RBF kernels, are amongst the most pop...