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On Implied Volatility for Options – Some Reasons to Smile and More to Correct
Bias correction Implied volatility,Kernel estimator Pricing errors
2016/1/25
We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in...
Limit Theorems for Some Critical Superprocesses
Superprocess critical superprocess non-extinction rate central limit theorem
2016/1/20
In this paper we establish some conditional limit theorems for some critical superprocesses X = {X t ,t ≥ 0}. First we identify the rate of non-extinction. Then we show that, for a large class of func...
Support Vector Machines,Kernel Logistic Regression,and Boosting
Support Vector Machines Kernel Logistic Regression Boosting
2015/8/21
Support Vector Machines,Kernel Logistic Regression,and Boosting.
Classification of Gene Microarrays by P enalized Logisti Regression
cancer diagnosis feature selection logistic regression microarray support vector machines
2015/8/21
Classification of Gene Microarrays by P enalized Logisti Regression.
Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
We construct a prequential test of probabilistic forecasts that does not reject correct forecasts when the data-generating processes is exchangeable and is not manipulable by a false forecaster.
On model selection consistency of M-estimators with geometrically decomposable penalties
model selection consistency M-estimators geometrically decomposable penalties
2013/6/14
Penalized M-estimators are used in many areas of science and engineering to fit models with some low-dimensional structure in high-dimensional settings. In many problems arising in bioinformatics, sig...
Estimation of False Discovery Proportion with Unknown Dependence
Approximate factor model large-scale multiple testing dependent test statistics un-known covariance matrix false discovery proportion
2013/6/14
Large-scale multiple testing with highly correlated test statistics arises frequently in many scienti?c research. Incorporating correlation information in estimating false discovery proportion has att...
Optimal Rates of Convergence of Transelliptical Component Analysis
Transelliptical component analysis Optimal rates of convergence Double asymptotics Minimax lower bound Elliptical copula
2013/6/14
Han and Liu (2012) proposed a method named transelliptical component analysis (TCA) for conducting scale-invariant principal component analysis on high dimensional data with transelliptical distributi...
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Dynamic Clustering Asymptotics Dependent Dirichlet Process Mixture
2013/6/17
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The alg...
Monotone false discovery rate
adaptive decision rule false discovery rate empirical Bayes methods mode matching isotonic regression
2013/6/14
This paper proposes a simple procedure to obtain monotone estimates of both the local and the tail false discovery rates that arise in large-scale multiple testing. The proposed monotonization natural...
Optimal rates of convergence for persistence diagrams in Topological Data Analysis
Optimal rates convergence persistence diagrams Topological Data Analysis
2013/6/14
Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appear...
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
A note on monitoring ratios of Weibull percentiles
Bayesian Estimators Statistical Process Control Weibull Distrib ution
2013/6/14
This note introduces a new Bayesian control chart to compare two processes by monitoring the ratio of their percentiles under Weibull assumption. Both in-control and out-of-control parameters are supp...
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
LDHMMs sequential data variational inference variational EM behavior modeling sequence classification
2013/6/14
This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each sequence is generat...
