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BEGIN:VEVENT
DTSTAMP:20260428T040151Z
DTSTART;VALUE=DATE-TIME:20140116T140000
DTEND;VALUE=DATE-TIME:20140116T160000
SUMMARY:CRiSM Seminar - Chenlei Leng (ÌÇÐÄTV)\, John Fox (Oxford & UCL/R
 oyal Free Hospital)
TZID:Europe/London
UID:20140116-094d43d541a2afde0141c1e00ee62bda@warwick.ac.uk
CREATED:20131016T152628Z
DESCRIPTION:John Fox (Oxford & UCL/Royal Free Hospital) Arguing logically
  about risks: strengths\, limitations and a request for assistance Abstr
 act: The standard mathematical treatment of risk combines numerical meas
 ures of uncertainty (usually probabilistic) and loss (money and other na
 tural estimators of utility). There are significant practical and theore
 tical problems with this interpretation. A particular concern is that th
 e estimation of quantitative parameters is frequently problematic\, part
 icularly when dealing with one-off events such as political\, economic o
 r environmental disasters. Consequently practical decision-making under 
 risk often requires extensions to the standard treatment. An intuitive a
 pproach to reasoning under uncertainty has recently become established i
 n computer science and cognitive science based on argumentation theory. 
 On this approach theories about an application domain (formalised in a n
 on-classical first-order logic) are applied to propositional facts about
  specific situations\, and arguments are constructed for and/or against 
 claims about what might happen in those situations. Arguments can also a
 ttack or support other arguments. Collections of arguments can be aggreg
 ated to characterize the type or degree of risk\, based on the grounds o
 f the arguments. The grounds and form of an argument can also be used to
  explain the supporting evidence for competing claims and assess their r
 elative credibility. This approach has led to a novel framework for deve
 loping versatile risk management systems and has been validated in a num
 ber of domains\, including clinical medicine and toxicology (e.g. www.in
 fermed.com\; www.lhasa.com). Argumentation frameworks are also being use
 d to support open discussion and debates about important issues (e.g. se
 e debate on "planet under pressure" at http://debategraph.org/Stream.asp
 x?nid=145319&vt=bubble&dc=focus). Despite the practical success of argum
 entation methods in risk management and other kinds of decision making t
 he main theories ignore quantitative measurement of uncertainty\, or the
 y combine qualitative reasoning with quantitative uncertainty in ad hoc 
 ways. After a brief introduction to argumentation theory I will demonstr
 ate some medical applications and invite suggestions for ways of incorpo
 rating uncertainty probabilistically that are mathematically satisfactor
 y. Chenlei Leng (ÌÇÐÄTV) High dimensional influence measure Influence d
 iagnosis is important since presence of influential observations could l
 ead to distorted analysis and misleading interpretations. For high-dimen
 sional data\, it is particularly so\, as the increased dimensionality an
 d complexity may amplify both the chance of an observation being influen
 tial\, and its potential impact on the analysis. In this article\, we pr
 opose a novel high-dimensional influence measure for regressions with th
 e number of predictors far exceeding the sample size. Our proposal can b
 e viewed as a high-dimensional counterpart to the classical Cook's dista
 nce. However\, whereas the Cook's distance quantifies the individual obs
 ervation's influence on the least squares regression coefficient estimat
 e\, our new diagnosis measure captures the influence on the marginal cor
 relations\, which in turn exerts serious influence on downstream analysi
 s including coefficient estimation\, variable selection and screening. M
 oreover\, we establish the asymptotic distribution of the proposed influ
 ence measure by letting the predictor dimension go to infinity. Availabi
 lity of this asymptotic distribution leads to a principled rule to deter
 mine the critical value for influential observation detection. Both simu
 lations and real data analysis demonstrate usefulness of the new influen
 ce diagnosis measure. This is joint work with Junlong Zhao\, Lexin Li\, 
 and Hansheng Wang. A copy of the paper is downloadable from http://arxiv
 .org/abs/1311.6636.
LOCATION:A1.01
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20131016T152628Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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