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Discussion of Random Projection Ensemble Classificationby Timothy I.…
www.statslab.cam.ac.uk/~rds37/papers/CHEN_SHAH.pdf2 Jan 2020: 1012. 14. Ionosphere. 50 100 200. 89. 1011. 1213. 1415. Musk. ... 3540. Eye state. 50 200 1000. Fig. 1. Misclassification rates and the corresponding confidence intervals of the original randomprojection ensemble classifier (grey) and the multiple random -
8. Lecture 9. Tests of goodness-of-fit and independence Lecture ...
www.statslab.cam.ac.uk/~sb2116/Statistics_IB/slides/S1B-17-09-fit-independence.pdf4 Feb 2020: We obtain:New car. oij Large Medium SmallPrevious Large 56 52 42 150car Medium 50 83 67 200. ... Previous Large 37.2 55.8 57.0 150car Medium 49.6 74.4 76.0 200. -
Math. Stat. Learn. 2 (2019), 165–216DOI 10.4171/MSL/14 Mathematical…
www.statslab.cam.ac.uk/~nickl/Site/__files/MSL.pdf24 Sep 2020: Math. Stat. Learn. 2 (2019), 165–216DOI 10.4171/MSL/14. Mathematical Statistics and Learning European Mathematical Society. On statistical Calderón problems. Kweku Abraham and Richard Nickl. Abstract. For D a bounded domain in Rd;d 2; with smooth -
Abstract We survey the published work of Harry Kesten ...
www.statslab.cam.ac.uk/~grg/papers/kesten-ptrf-final.pdf13 Nov 2020: Abstract We survey the published work of Harry Kesten in probability theory,with emphasis on his contributions to random walks, branching processes, perco-lation, and related topics. A complete bibliography is included of his publications. Keywords -
4. Lecture 5. Confidence Intervals Lecture 5. Confidence Intervals ...
www.statslab.cam.ac.uk/~sb2116/Statistics_IB/slides/S1B-17-05-confidence-intervals.pdf4 Feb 2020: What might the true proportion be? We assume we have an observation of x = 200 from a Binomial(n,p)distribution with n = 1, 000. -
Mathematical Foundations of Infinite-Dimensional Statistical Models
www.statslab.cam.ac.uk/~nickl/Site/__files/FULLPDF.pdf25 Feb 2020: Mathematical Foundations of Infinite-DimensionalStatistical Models. In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood and Bayesianposterior inference does
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