Events
Fri 17 May, '13- |
Algorithms & Computationally Intensive Inference SeminarsAA1.01 |
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Tue 21 May, '13- |
Young Researchers MeetingC0.06 Stats Common Rm |
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Wed 22 May, '13- |
SF@W SeminarA1.01 |
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Wed 22 May, '13- |
VC Visit together with RegistrarB3.02 (Maths) |
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Wed 22 May, '13- |
Measured Value Reading GroupD1.07 |
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Thu 23 May, '13- |
FK Reading GroupA1.01 |
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Thu 23 May, '13- |
NeuroStats Reading GroupA1.01 |
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Thu 23 May, '13- |
CRiSM Seminar - Ioanna ManolopoulouA1.01Ioanna Manolopoulou (University College London) Bayesian observation modeling in presence-only data The prevalence of presence-only samples eg. in ecology or criminology has led to a variety of statistical approaches. Aiming to predict ecological niches, species distribution models provide probability estimates of a binary response (presence/absence) in light of a set of environmental covariates. Similarly, statistical models to predict crime use propensity indicators from observable attributes inferred from incidental data. However, the associated challenges are confounded by non-uniform observation models; even in cases where observation is driven by seemingly irrelevant factors, these may distort estimates about the distribution of occurrences as a function of covariates due to unknown correlations. We present a Bayesian non-parametric approach to addressing sampling bias by carefully incorporating an observation model in a partially identifiable framework with selectively informative priors and linking it to the underlying process. Any available information about the role of various covariates in the observation process can then naturally enter the model. For example, in cases where sampling is driven by presumed likelihood of detecting an occurrence, the observation model becomes a proxy of the presence/absence model. We illustrate our methods on an example from species distribution modeling and a corporate accounting application. Joint work with Richard Hahn from Chicago Booth. |
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Fri 24 May, '13- |
Algorithms & Computationally Intensive Inference SeminarsAA1.01 |
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Tue 28 May, '13- |
Young Researchers MeetingC0.06 Stats Common Rm |
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Wed 29 May, '13- |
SF@W SeminarA1.01 |
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Wed 29 May, '13- |
Measured Value Reading GroupD1.07 |
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Thu 30 May, '13- |
FK Reading GroupA1.01 |
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Thu 30 May, '13- |
CRiSM Seminar - Tom PalmerA1.01Tom Palmer (Warwick Medical School) Topics in instrumental variable estimation: structural mean models and bounds One aim of epidemiological studies is to investigate the effect of a risk factor on a disease outcome. However, these studies are prone to unmeasured confounding and reverse causation. The use of genotypes as instrumental variables, known as Mendelian randomization studies, are one way to overcome this. In this talk I describe some methods in instrumental variable estimation; structural mean models and nonparametric bounds for the average causal effect. Specifically, I describe how to estimate structural mean models using multiple instrumental variables in the generalized method of moments framework common in Econometrics. I describe the nonparametric bounds for the average causal effect of Balke and Pearl (JASA, 1997) which can be applied when each of the three variables; instrument, intermediate, and outcome are all binary. I describe some methodological extensions to these bounds and their limitations. To demonstrate the models I use a Mendelian randomization example investigating the effect of being overweight on the risk of hypertension in the Copenhagen General Population Study. I will also draw some comparisons with the application of instrumental variables to correct for noncompliance in randomized controlled trials. |
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Fri 31 May, '13- |
Algorithms & Computationally Intensive Inference SeminarsAA1.01 |
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Tue 4 Jun, '13- |
Young Researchers MeetingC0.06 Stats Common Rm |
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Wed 5 Jun, '13- |
SF@W SeminarA1.01 |
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Wed 5 Jun, '13- |
Measured Value Reading GroupB3.01 (Maths) |
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Thu 6 Jun, '13- |
FK Reading GroupA1.01 |
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Thu 6 Jun, '13- |
Staff LunchC0.06 Common Room |
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Thu 6 Jun, '13- |
CRiSM Seminar - Ajay JasraA1.01Ajay Jasra (National University of Singapore) On the convergence of Adaptive sequential Monte Carlo Methods In several implementations of Sequential Monte Carlo (SMC) methods, it is natural and important in terms of algorithmic efficiency, to exploit the information on the history of the particles to optimally tune their subsequent propagations. In the following talk we provide an asymptotic theory for a class of such adaptive SMC methods. Our theoretical framework developed here will cover for instance, under assumptions, the algorithms in Chopin (2002), Jasra et al (2011), Schafer & Chopin (2013). There are limited results about the theoretical underpinning of such adaptive methods: we will bridge this gap by providing a weak law of large numbers (WLLN) and a central limit theorem (CLT) for some of the algorithms. The latter seems to be the first result of its kind in the literature and provides a formal justification of algorithms that are used in many practical scenarios. This is a joint work with Alex Beskos (NUS/UCL). |
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Fri 7 Jun, '13- |
Algorithms & Computationally Intensive Inference SeminarsAA1.01 |
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Tue 11 Jun, '13- |
Young Researchers MeetingC0.06 Stats Common Rm |
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Wed 12 Jun, '13- |
SF@W SeminarA1.01 |
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Wed 12 Jun, '13- |
Measured Value Reading GroupD1.07 |
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Thu 13 Jun, '13- |
Research Fellow Candidates PresentationsA1.01 |
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Thu 13 Jun, '13- |
CRiSM Seminar - Piotr FryzlewiczA1.01Piotr Fryzlewicz (London School of Economics) Wild Binary Segmentation for multiple change-point detection |
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Fri 14 Jun, '13- |
Algorithms & Computationally Intensive Inference SeminarsAA1.01 |
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Tue 18 Jun, '13- |
Young Researchers MeetingC0.06 Stats Common Rm |
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Wed 19 Jun, '13- |
SF@W SeminarA1.01 |