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Full-Text Articles in Statistics and Probability
Asymptotic Theory For Cross-Validated Targeted Maximum Likelihood Estimation, Wenjing Zheng, Mark J. Van Der Laan
Asymptotic Theory For Cross-Validated Targeted Maximum Likelihood Estimation, Wenjing Zheng, Mark J. Van Der Laan
Wenjing Zheng
We consider a targeted maximum likelihood estimator of a path-wise differentiable parameter of the data generating distribution in a semi-parametric model based on observing n independent and identically distributed observations. The targeted maximum likelihood estimator (TMLE) uses V-fold sample splitting for the initial estimator in order to make the TMLE maximally robust in its bias reduction step. We prove a general theorem that states asymptotic efficiency (and thereby regularity) of the targeted maximum likelihood estimator when the initial estimator is consistent and a second order term converges to zero in probability at a rate faster than the square root of …
Cross-Validated Targeted Minimum-Loss-Based Estimation, Wenjing Zheng, Mark Van Der Laan
Cross-Validated Targeted Minimum-Loss-Based Estimation, Wenjing Zheng, Mark Van Der Laan
Wenjing Zheng
No abstract provided.
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Douglas G. Steigerwald
We study the finite-sample performance of test statistics in linear regression models where the error dependence is of unknown form. With an unknown dependence structure there is traditionally a trade-off between the maximum lag over which the correlation is estimated (the bandwidth) and the amount of heterogeneity in the process. When allowing for heterogeneity, through conditional heteroskedasticity, the correlation at far lags is generally omitted and the resultant inflation of the empirical size of test statistics has long been recognized. To allow for correlation at far lags we study test statistics constructed under the possibly misspecified assumption of conditional homoskedasticity. …
The Underground Economy Of Fake Antivirus Software, Douglas Steigerwald, Brett Stone-Gross, Ryan Abman, Richard Kemmerer, Christopher Kruegel, Giovanni Vigna
The Underground Economy Of Fake Antivirus Software, Douglas Steigerwald, Brett Stone-Gross, Ryan Abman, Richard Kemmerer, Christopher Kruegel, Giovanni Vigna
Douglas G. Steigerwald
Fake antivirus (AV) programs have been utilized to defraud millions of computer users into paying as much as one hundred dollars for a phony software license. As a result, fake AV software has evolved into one of the most lucrative criminal operations on the Internet. In this paper, we examine the operations of three large-scale fake AV businesses, lasting from three months to more than two years. More precisely, we present the results of our analysis on a trove of data obtained from several backend servers that the cybercriminals used to drive their scam operations. Our investigations reveal that these …
Computing Highly Accurate Confidence Limits From Discrete Data Using Importance Sampling, Chris Lloyd
Computing Highly Accurate Confidence Limits From Discrete Data Using Importance Sampling, Chris Lloyd
Chris J. Lloyd
For discrete parametric models, approximate confidence limits perform poorly from a strict frequentist perspective. In principle, exact and optimal confidence limits can be computed using the formula of Buehler (1957), Lloyd and Kabaila (2003). So-called profile upper limits (Kabaila \& Lloyd, 2001) are closely related to Buehler limits and have extremely good properties. Both profile and Buehler limits depend on the probability of a certain tail set as a function of the unknown parameters. Unfortunately, this probability surface is not computable for realistic models. In this paper, importance sampling is used to estimate the surface and hence the confidence limits. …