T ˆ β as ˆ se ˆ μ x h x T H T H 1 h x 1 2 ˆ σ c 2019 The Trustees of the

T ˆ β as ˆ se ˆ μ x h x t h t h 1 h x 1 2 ˆ σ

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T ˆ β as: ˆ se μ ( x )) = ( h ( x ) T ( H T H ) - 1 h ( x )) 1 2 ˆ σ c 2019 The Trustees of the Stevens Institute of Technology
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Bootstrap Methods EM Algorithm To bootstrap, draw B datasets each of size N with replacement from the training data. To each bootstrapped data set Z * , fit a cubic spline ˆ μ * ( x ) . Using B = 200, we can determine a 95 % confidence interval. This is an example of a non-parametric bootstrap. c 2019 The Trustees of the Stevens Institute of Technology
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Elements of Statistical Learning (2nd Ed.) c Hastie, Tibshirani & Friedman 2009 Chap 8 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1 0 1 2 3 4 5 •• • ••• •••• •• •• x y 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1 0 1 2 3 4 5 x y •• • ••• •••• •• •• 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1 0 1 2 3 4 5 x y •• • ••• •••• •• •• 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1 0 1 2 3 4 5 x y •• • ••• •••• •• •• FIGURE 8.2. (Top left:) B -spline smooth of data. (Top right:) B -spline smooth plus and minus 1 . 96 × standard error bands. (Bottom left:) Ten bootstrap replicates of the B -spline smooth. (Bottom right:) B -spline smooth with 95 % standard error bands com- puted from the bootstrap distribution.
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Bootstrap Methods EM Algorithm Parametric Bootstrap Lets assume that the model errors are Gaussian, this leads us to: Y = μ ( X ) + ε ; ε N ( 0 , σ 2 ) , μ ( x ) = 7 X j = 1 β j h j ( x ) c 2019 The Trustees of the Stevens Institute of Technology
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Bootstrap Methods EM Algorithm We simulate new responses by adding Gaussian noise to the predicted values, y * i = ˆ μ * ( x i ) + ε * i ; ε * i N ( 0 , ˆ σ 2 ); i = 1 , . . . , N The process is repeated B times and the the resulting bootstrap datasets, ( x 1 , y * 1 ) , . . . , ( x N , y * N ) have the smoothing spline fit on each. c 2019 The Trustees of the Stevens Institute of Technology
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Bootstrap Methods EM Algorithm Simple Example Assume we have a mixture of two normal random variables, X 1 and X 2 . Looking at their histogram, we can see that a single normal would be a very bad fit. c 2019 The Trustees of the Stevens Institute of Technology
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Bootstrap Methods EM Algorithm We attempt to model this using a mixture of two normal distributions given by: Y = ( 1 - Δ) Y 1 + Δ Y 2 where Δ ∈ { 0 , 1 } , with P (Δ = 1 ) = π . If we let φ θ ( x ) be the normal density with parameters θ , then the density of Y is: g Y ( y ) = ( 1 - π ) φ θ 1 ( y ) + πφ θ 2 ( y ) c 2019 The Trustees of the Stevens Institute of Technology
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Bootstrap Methods EM Algorithm If we want to fit this using MLE, we need to estimate the parameters: θ = ( π, θ 1 , θ 2 ) = ( π, μ 1 , σ 2 1 , μ 2 , σ 2 2 ) The log-likelihood based on the N cases is: ( θ ; Z ) = N X i = 1 log[( 1 - π ) φ θ 1 ( y
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