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STATISTICAL MODELLING Part IIC / Michaelmas 2022Example Sheet 1 ...
www.statslab.cam.ac.uk/~qz280/teaching/modelling-2022/E1.pdf3 Jun 2024: In all the questions that follow, X is an n by p design matrix with full column rank and H is theorthogonal projection on to the column space of X. ... Show that for all x Rn, ‖x‖2 ‖ΠWx‖2 ‖ΠV x‖2. (b) Consider the normal linear model with a -
MATHEMATICS OF MACHINE LEARNING Part IIExample Sheet 2 (of ...
www.statslab.cam.ac.uk/~rds37/teaching/machine_learning/Qu2.pdf20 Feb 2024: 3. Let F be the set of all polynomials of degree at most 2 on X = Rp. ... Then g : Rm R. given by g(x) = f(Ax b) is a convex function.(c) Let Cα Rd be convex for all α I where I is some index set. -
STATISTICAL MODELLING Part IIC / Michaelmas 2022Example Sheet 3 ...
www.statslab.cam.ac.uk/~qz280/teaching/modelling-2022/E3.pdf3 Jun 2024: f(y; µ,σ2) =1. 2πσ2e. (yµ)2. 2σ2 ,y R. (b) The Gamma distribution, Gamma(α,β):. ... 2 = 2/σ2βj for all j). (c) How do the expressions in (a) and (b) simplify when g(µi) is the canonical link function? -
MATHEMATICS OF MACHINE LEARNING Part IIExample Sheet 3 (of ...
www.statslab.cam.ac.uk/~rds37/teaching/machine_learning/Qu3.pdf17 Jan 2024: xπ)>(z π) 0 for all z C. 4. Show that ‖β‖1 = {b : for each j,bj [1, 1] and bj = sgn(βj) if βj 6= 0}. ... c) Let. H =. {Mm=1. βmhm : ‖β‖1 1, hm B for m = 1,. -
CAUSAL INFERENCE Part III / Michaelmas 2023Example Sheet 2 ...
www.statslab.cam.ac.uk/~qz280/teaching/causal-2023/E2.pdf3 Jun 2024: j, j) B for all j V) or an directed acyclic graph (DAG) G = (V,D). ... Show that there exists a trek from j to k via U(meaning all its non-endpoints are in U) if and only if there exists an arc from j to -
Confounder Selection via Iterative Graph Expansion Qingyuan Zhao…
www.statslab.cam.ac.uk/~qz280/talk/lse-2024/slides.pdf3 Jun 2024: adjustment set for A, B given S′ in G. Then every element in the output ofConfounderSelect(X, Y ) is a sufficient adjustment set for (X, Y ). 2 Completeness (all minimal primary ... all minimal sufficiency): Suppose further thatFindPrimary((A, B); S′) -
Confounder Selection via (Iterative) Graph Expansion Qingyuan Zhao…
www.statslab.cam.ac.uk/~qz280/talk/pcic-2023/slides.pdf3 Jun 2024: adjustment set for A, B given S′ in G. Then every element in the output ofConfounderSelect(X, Y ) is a sufficient adjustment set for (X, Y ). 2 Completeness (all minimal primary ... all minimal sufficiency): Suppose further thatFindPrimary((A, B); S′) -
MR Data Challenge 2019 — The role of lipoprotein subfractions in…
www.statslab.cam.ac.uk/~qz280/publication/mr-lipids/report.pdf3 Jun 2024: out$pval.sel <- pval.sel. out.all <- rbind(out.all, out)}. 6. Qingyuan Zhao. Qingyuan Zhao. ... library(ggplot2). ggplot(out.all) aes(x = method, y = b, ymin = b - 1.96 se, ymax = b 1.96 se,col = pval.adjusted < 0.05). -
MR Data Challenge 2019 — The role of lipoprotein subfractions in…
www.statslab.cam.ac.uk/~qz280/talk/mr-raps-markdown/report.pdf3 Jun 2024: out$pval.sel <- pval.sel. out.all <- rbind(out.all, out)}. 6. Qingyuan Zhao. Qingyuan Zhao. ... library(ggplot2). ggplot(out.all) aes(x = method, y = b, ymin = b - 1.96 se, ymax = b 1.96 se,col = pval.adjusted < 0.05). -
2-nhb.dvi
www.statslab.cam.ac.uk/~grg/books/hammfest/2-nhb.pdf22 Mar 2024: n ) (E(λ)) for some (all) λ > 0,(iii) Xn µ a.s. ... All remainders are power series,so may be differentiated term-wise. The exponential estimates obtainedabove are at worst multiplied by polynomials.
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