We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online., DRI and FR acknowledge the support from the Spanish Ministry ofEconomy and Innovation program MTM2017-86875-C3-1-R and the ESF-COST Action IS1304 on Expert Judgement. FR also acknowledges thecontribution of the Community of Madrid through its Chair of Excellenceprogramme. JGO’s research is financed by the Spanish Ministry of Economyand Competitiveness under FPI SO grant agreement BES-2015-072892.This work has also been partially supported by the Spanish Ministry ofEconomy and Competitiveness through the “Severo Ochoa” Program forCenters of Excellence in R&D (SEV-2015-0554) and project MTM2015-72907-EXP., Peer reviewed, Peer Reviewed

Classification techniques are widely used in security settings in which data can be deliberately manipulated by an adversary trying to evade detection and achieve some benefit. However, traditional classification systems are not robust to such data modifications. Most attempts to enhance classification algorithms in adversarial environments have focused on game theoretical ideas under strong underlying common knowledge assumptions, which are not actually realistic in security domains. We provide an alternative framework to such problems based on adversarial risk analysis which we illustrate with examples. Computational, implementation and robustness issues are discussed., R.N. acknowledges support the Spanish Ministry for his grant FPU15-03636. The work of D.R.I. is supported by the Spanish Ministry program MTM2017-86875-C3-1-R and the AXA-ICMAT Chair on Adversarial Risk Analysis. This work has also been partially supported by the Spanish Ministry of Economy through the Severo Ochoa Program for Centers of Excellence in R&D (SEV-2015-0554), the project MTM2015-72907-EXP and the EU's Horizon 2020 project 740920 CYBECO (Supporting Cyberinsurance from a Behavioural Choice Perspective). F.R. acknowledges the contribution of the Comunidad de Madrid through its Chair of Excellence programme. We are grateful for the suggestions of the referees., Peer Reviewed

Insider threats entail major security issues in many organizations. Game theoretic models of insider threats so far proposed do not take into account important factors such as the organizational culture and whether the attacker was detected or not. They also fail to model defensive mechanisms already put in place by an organization to mitigate insider attacks. We propose two new models which incorporate these settings and, hence, are more realistic, and use adversarial risk analysis to find their solutions. Our models and solutions are general and can be applied to most insider threat scenarios. A data security example illustrates the discussion., The work of CJ was supported by the Strategic Investment funding provided by the University of Waikato. The work of DRI is supported by the AXA-ICMAT Chair on Adversarial Risk Analysis, the Spanish Ministry of Economy and Innovation program MTM2017-86875-C3-1-R and project MTM2015-72907-EXP. Work supported by the EU’s Horizon 2020 project 740920 CYBECO (Supporting Cyberinsurance from a Behavioural Choice Perspective).

This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original EB procedure of Molina and Rao [Small area estimation of poverty indicators. Canadian J Stat. 2010;38(3):369–385], including heteroskedasticity and survey weights, but using a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased and noisier point estimates. The document presents an update to the World Bank’s EB implementation by considering the original EB procedures for point and noise estimation, extended for complex designs and heteroscedasticity. Simulation experiments illustrate that the revised methods yield considerably less biased and more efficient estimators than those obtained from the clustered bootstrap approach.