kubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link functi
|
|
- きょうすけ たもん
- 5 years ago
- Views:
Transcription
1 kubostat7f p statistaical models appeared in the class 7 (f) kubo@eeshokudaiacjp 7 : 7 : The development of linear models Hierarchical Baesian Model Be more flexible Generalized Linear Mixed Model (GLMM) ncoporating random effects such as individualit parameter estimation MCMC MLE Generalized Linear Model (GLM) Alwas normal distribution? That's non-sense! MSE Linear model Kubo Doctrine: Learn the evolution of linear-model famil, firstl! kubostat7f ( 7 (f) 7 / 3 kubostat7f ( 7 (f) 7 / 3 GLM! overdispersion caused b individual differences 3 r i GLMM 7 (GLMM) : : kubostat7f ( 7 (f) 7 3 / 3 kubostat7f ( 7 (f) 7 / 3 GLM! example : GLM! seed survivorship again, but?! GLM! (overdispersion)? (A) i N i = i = 3 x i {, 3,,, } alive seeds i (B) x i i 3 number of leaves x i kubostat7f ( 7 (f) 7 / 3 kubostat7f ( 7 (f) 7 / 3
2 kubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link function : logit number of alive seeds i 3 number of leaves x i number of alive seeds i underestimated β (A) β 3 number of leaves x i Not binomial! (B)! x i = i 3 i kubostat7f ( 7 (f) 7 7 / 3 kubostat7f ( 7 (f) 7 / 3 overdispersion caused b individual differences overdispersion caused b individual differences (overdispersion)? overdispersion caused b individual differences unobservable differences? kubostat7f ( 7 (f) 7 9 / 3 (A) Not or less overdispersed (B) Overdispersed!! i i kubostat7f ( 7 (f) 7 / 3 overdispersion caused b individual differences overdispersion caused b individual differences GLM GLM does not take into account individual differences Almost all real data are overdispersed! kubostat7f ( 7 (f) 7 / 3 kubostat7f ( 7 (f) 7 / 3
3 kubostat7f p3 an improvement of logistic regression model 3 fixed effects random effects probabilit distribution binomial distribution : : β + β x i + r i link function : logit number of alive seeds i 3 number of leaves x i kubostat7f ( 7 (f) 7 3 / 3 kubostat7f ( 7 (f) 7 / 3 i r i suppose {r i} follow the Gausssian distribution {r i } qi r i > r i = r i < 3 x i s = s = s = 3 r i ) p(r i s) = ( exp r i πs s p(r i s) r i r i r i kubostat7f ( 7 (f) 7 / 3 kubostat7f ( 7 (f) 7 / 3 r i a numerical experiment using random numbers (A) (B) p(r i s) s = {r i} s = i r i q i = +exp( r i) p( i q i) i r i > # defining logistic function > logistic <- function(z) { / ( + exp(-z)) } > # random numbers following binomial distribution > rbinom(,, prob = logistic()) > # random numbers following Gausssian distribution > rnorm(, mu =, sd = ) > r <- rnorm(, mu =, sd = ) > # random numbers following? > rbinom(,, prob = logistic( + r)) kubostat7f ( 7 (f) 7 7 / 3 kubostat7f ( 7 (f) 7 / 3
4 kubostat7f p fixed effects random effects global parameter local parameter Mixed : β + β x i + r i fixed effects: β + β x i random effects: +r i fixed? random?? Mixed : β + β x i + r i fixed effects: β + β x i global parameter for all individuals s global parameter random effects: +r i local parameter onl for individual i kubostat7f ( 7 (f) 7 9 / 3 kubostat7f ( 7 (f) 7 / 3 r i r i r i r i local parameters: {r, r,, r } r i saturation model > d <- readcsv("datacsv") > head(d) N x id 3 3 kubostat7f ( 7 (f) 7 / 3 kubostat7f ( 7 (f) 7 / 3 r i r i r i binomial distribution i ( ) p( i β, β ) = q i i ( q i) i i Gaussian distribution r i p(r i s) = πs exp ( r i s i likelihood to remove r i r i L i = p( i β, β, r i ) p(r i s)dr i likedhood for all data β, β, s L(β, β, s) = i kubostat7f ( 7 (f) 7 3 / 3 L i ) global parameter local parameter Mixed : β + β x i + r i global parameter fixed effects: β, β : s local parameter random effects: {r, r,, r } kubostat7f ( 7 (f) 7 / 3
5 kubostat7f p 個体差 r ごとに異なる 二項分布 個体差 ri について積分する ということは 二項分布と正規分布をまぜ あわせること 集団内の r の分布 q = kubostat7f ( 統計モデリング入門 7 (f) 個体差 r ごとに異なる ポアソン分布 r = λ = r = 3 λ = r = λ = 7 r = 3 λ = kubostat7f ( 7 / 3 r 積分 r 集団全体をあらわす 混合された分布 r r p(r) = 9 - r 統計モデリング入門 7 (f) 7 / 3 > > > + installpackages("glmmml") # if ou don t have glmmml librar(glmmml) glmmml(cbind(, N - ) ~ x, data = d, famil = binomial, cluster = id) > d <- readcsv("datacsv") > head(d) N x id 3 3 p(r) = p(r) = Poisson and Gaussian distributions p(r) = r glmmml package を使って GLMM の推定 ポアソン分布と正規分布のまぜあわせ - - 重み p(r s) p(r) = kubostat7f ( 集団内の r の分布 r - q = 93 ntegral of ri mixture distribution of the binomial and Gaussian distributions - 集団全体をあらわす 混合された分布 p(r) = 3 r = 積分 q = 73 r p(r) = 3 r = binomial and Gaussian distributions 二項分布と正規分布のまぜあわせ - q = 3 p(r) = r = 重み p(r s) r = 統計モデリング入門 7 (f) 7 7 / 3 kubostat7f ( 統計モデリング入門 7 (f) 7 / 3 prediction estimates GLMM の 推定値 : β, βˆ, s 推定された GLMM を使った 予測 (B) 葉数 x = での種子数分布 3 個体数 生存種子数 i (A) 葉数と生存種子数の関係 > glmmml(cbind(, N - ) ~ x, data = d, famil = binomial, + cluster = id) (snip) coef se(coef) z Pr(> z ) (ntercept) e- x 99 3e- 9 gaussian 39 Scale parameter in mixing distribution: Std Error: Residual deviance: on 97 degrees of freedom 統計モデリング入門 7 (f) 7 葉数 xi AC: 7 β = 3, βˆ = 99, s = 9 kubostat7f ( 3 9 / 3 kubostat7f ( 種子数 統計モデリング入門 7 (f) 7 3 / 3
6 kubostat7f p GLMM GLMM differences both in plants and pots + GLMM GLMM (A) (B) logitq i = β + β x i (GLM) q i: logitq i = β + β x i + r i (A) (B) kubostat7f ( 7 (f) 7 3 / 3 kubostat7f ( 7 (f) 7 3 / 3 GLMM differences both in plants and pots + GLMM GLMM GLMM summar (C) (D) logitq i = β + β x i + r j logitq i = β + β x i + r i + r j random effects global parameter local parameter GLMM global parameter local parameter local parameter (eg + ) kubostat7f ( 7 (f) 7 33 / 3 kubostat7f ( 7 (f) 7 3 / 3 GLMM Ya! Be more flexible The next topic The development of linear models Hierarchical Baesian Model Generalized Linear Mixed Model (GLMM) ncoporating random effects such as individualit parameter estimation MCMC MLE Generalized Linear Model (GLM) Alwas normal distribution? That's non-sense! MSE Linear model Hierarchical Baesiam Model (HBM) kubostat7f ( 7 (f) 7 3 / 3
一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM
.. ( ) (2) GLMM kubo@ees.hokudai.ac.jp I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!
More informationkubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :
kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)
More informationkubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib
kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit
More informationkubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or
kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4
More informationkubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : :
kubostat2017c p.1 2017 (c), a generalized linear model (GLM) : kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 kubostat2017c (http://goo.gl/76c4i) 2017 (c) 2017 11 14 1 / 47 agenda
More informationkubostat2017j p.2 CSV CSV (!) d2.csv d2.csv,, 286,0,A 85,0,B 378,1,A 148,1,B ( :27 ) 10/ 51 kubostat2017j (http://goo.gl/76c4i
kubostat2017j p.1 2017 (j) Categorical Data Analsis kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 15 : 2017 11 08 17:11 kubostat2017j (http://goo.gl/76c4i) 2017 (j) 2017 11 15 1 / 63 A B C D E F G
More information12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
More informationkubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
More information講義のーと : データ解析のための統計モデリング. 第3回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
More informationkubostat1g p. MCMC binomial distribution q MCMC : i N i y i p(y i q = ( Ni y i q y i (1 q N i y i, q {y i } q likelihood q L(q {y i } = i=1 p(y i q 1
kubostat1g p.1 1 (g Hierarchical Bayesian Model kubo@ees.hokudai.ac.jp http://goo.gl/7ci The development of linear models Hierarchical Bayesian Model Be more flexible Generalized Linear Mixed Model (GLMM
More information60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77
60 (W30)? 1. ( ) kubo@ees.hokudai.ac.jp 2. ( ) web site URL http://goo.gl/e1cja!! 2013 03 07 (2013 03 07 17 :41 ) 1/ 77 ! : :? 2013 03 07 (2013 03 07 17 :41 ) 2/ 77 2013 03 07 (2013 03 07 17 :41 ) 3/ 77!!
More informationkubo2015ngt6 p.2 ( ( (MLE 8 y i L(q q log L(q q 0 ˆq log L(q / q = 0 q ˆq = = = * ˆq = 0.46 ( 8 y 0.46 y y y i kubo (ht
kubo2015ngt6 p.1 2015 (6 MCMC kubo@ees.hokudai.ac.jp, @KuboBook http://goo.gl/m8hsbm 1 ( 2 3 4 5 JAGS : 2015 05 18 16:48 kubo (http://goo.gl/m8hsbm 2015 (6 1 / 70 kubo (http://goo.gl/m8hsbm 2015 (6 2 /
More informationk2 ( :35 ) ( k2) (GLM) web web 1 :
2012 11 01 k2 (2012-10-26 16:35 ) 1 6 2 (2012 11 01 k2) (GLM) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 : 2 2 4 3 7 4 9 5 : 11 5.1................... 13 6 14 6.1......................
More informationkubostat2018a p.1 統計モデリング入門 2018 (a) The main language of this class is 生物多様性学特論 Japanese Sorry An overview: Statistical Modeling 観測されたパターンを説明する統計モデル
p.1 統計モデリング入門 2018 (a) The main language of this class is 生物多様性学特論 Japanese Sorry An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) Why in Japanese? because even in Japanese, statistics
More informationkubo2017sep16a p.1 ( 1 ) : : :55 kubo ( ( 1 ) / 10
kubo2017sep16a p.1 ( 1 ) kubo@ees.hokudai.ac.jp 2017 09 16 : http://goo.gl/8je5wh : 2017 09 13 16:55 kubo (http://goo.gl/ufq2) ( 1 ) 2017 09 16 1 / 106 kubo (http://goo.gl/ufq2) ( 1 ) 2017 09 16 2 / 106
More information統計モデリング入門 2018 (a) 生物多様性学特論 An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) 統計モデリング入門 2018a 1
統計モデリング入門 2018 (a) 生物多様性学特論 An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) kubo@ees.hokudai.ac.jp 1/56 The main language of this class is Japanese Sorry Why in Japanese? because
More information講義のーと : データ解析のための統計モデリング. 第2回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
More information講義のーと : データ解析のための統計モデリング. 第5回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
More information,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i
Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,
More information(2/24) : 1. R R R
R? http://hosho.ees.hokudai.ac.jp/ kubo/ce/2004/ : kubo@ees.hokudai.ac.jp (2/24) : 1. R 2. 3. R R (3/24)? 1. ( ) 2. ( I ) : (p ) : cf. (power) p? (4/24) p ( ) I p ( ) I? ( ) (5/24)? 0 2 4 6 8 A B A B (control)
More information/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
More information今回 次回の要点 あぶない 時系列データ解析は やめましょう! 統計モデル のあてはめ Danger!! (危 1) 時系列データの GLM あてはめ (危 2) 時系列Yt 時系列 Xt 各時刻の個体数 気温 とか これは次回)
生態学の時系列データ解析でよく見る あぶない モデリング 久保拓弥 mailto:kubo@ees.hokudai.ac.jp statistical model for time-series data 2017-07-03 kubostat2017 (h) 1/59 今回 次回の要点 あぶない 時系列データ解析は やめましょう! 統計モデル のあてはめ Danger!! (危 1) 時系列データの
More information80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = i=1 i=1 n λ x i e λ i=1 x i! = λ n i=1 x i e nλ n i=1 x
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
More information/ *1 *1 c Mike Gonzalez, October 14, Wikimedia Commons.
2010 05 22 1/ 35 2010 2010 05 22 *1 kubo@ees.hokudai.ac.jp *1 c Mike Gonzalez, October 14, 2007. Wikimedia Commons. 2010 05 22 2/ 35 1. 2. 3. 2010 05 22 3/ 35 : 1.? 2. 2010 05 22 4/ 35 1. 2010 05 22 5/
More information/ 60 : 1. GLM? 2. A: (pwer functin) x y?
2009-03-17 1/ 60 (2009-03-17) GLM 1. GLM :, link,, deviance (20 ) 2. GLM : (60 ) 3. GLM ( ): ffset (40 ) http://hsh.ees.hkudai.ac.jp/ kub/ce/ecsj2009.html 2009-03-17 2/ 60 : 1. GLM? 2. A: (pwer functin)
More information1 15 R Part : website:
1 15 R Part 4 2017 7 24 4 : website: email: http://www3.u-toyama.ac.jp/kkarato/ kkarato@eco.u-toyama.ac.jp 1 2 2 3 2.1............................... 3 2.2 2................................. 4 2.3................................
More informationAR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
More informationUse R
Use R! 2008/05/23( ) Index Introduction (GLM) ( ) R. Introduction R,, PLS,,, etc. 2. Correlation coefficient (Pearson s product moment correlation) r = Sxy Sxx Syy :, Sxy, Sxx= X, Syy Y 1.96 95% R cor(x,
More informationy i OLS [0, 1] OLS x i = (1, x 1,i,, x k,i ) β = (β 0, β 1,, β k ) G ( x i β) 1 G i 1 π i π i P {y i = 1 x i } = G (
7 2 2008 7 10 1 2 2 1.1 2............................................. 2 1.2 2.......................................... 2 1.3 2........................................ 3 1.4................................................
More information1 環境統計学ぷらす 第 5 回 一般 ( 化 ) 線形混合モデル 高木俊 2013/11/21
1 環境統計学ぷらす 第 5 回 一般 ( 化 ) 線形混合モデル 高木俊 shun.takagi@sci.toho-u.ac.jp 2013/11/21 2 予定 第 1 回 : Rの基礎と仮説検定 第 2 回 : 分散分析と回帰 第 3 回 : 一般線形モデル 交互作用 第 4.1 回 : 一般化線形モデル 第 4.2 回 : モデル選択 (11/29?) 第 5 回 : 一般化線形混合モデル
More information: (GLMM) (pseudo replication) ( ) ( ) & Markov Chain Monte Carlo (MCMC)? /30
PlotNet 6 ( ) 2006-01-19 TOEF(1998 2004), AM, growth6 DBH growth (mm) 1998 1999 2000 2001 2002 2003 2004 10 20 30 40 50 70 DBH (cm) 1. 2. - - : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/show/2006/plotnet/
More informationk3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k
2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................
More information% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti One-sample test of pr
1 1. 2014 6 2014 6 10 10% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti 1029 0.35 0.40 One-sample test of proportion x: Number of obs = 1029 Variable Mean Std.
More information橡ボーダーライン.PDF
1 ( ) ( ) 2 3 4 ( ) 5 6 7 8 9 10 11 12 13 14 ( ) 15 16 17 18 19 20 ( ) 21 22 23 24 ( ) 25 26 27 28 29 30 ( ) 31 To be or not to be 32 33 34 35 36 37 38 ( ) 39 40 41 42 43 44 45 46 47 48 ( ) 49 50 51 52
More information: Bradley-Terry Burczyk
58 (W15) 2011 03 09 kubo@ees.hokudai.ac.jp http://goo.gl/edzle 2011 03 09 (2011 03 09 19 :32 ) : Bradley-Terry Burczyk ? ( ) 1999 2010 9 R : 7 (1) 8 7??! 15 http://www.atmarkit.co.jp/fcoding/articles/stat/07/stat07a.html
More information/ 55 2 : : (GLM) 1. 1/23 ( )? GLM? (GLM ) 2.! 1/25 ( ) ffset (GLM )
2012 01 25 1/ 55 ( II) : (2012 1 ) 2 2 (GLM) 2012 01 25! kub@ees.hkudai.ac.jp http://g.gl/76c4i 2012 01 25 2/ 55 2 : : (GLM) 1. 1/23 ( )? GLM? (GLM ) 2.! 1/25 ( ) ffset (GLM ) 2012 01 25 3/ 55 1. : 2.
More informationdvi
2017 65 2 185 200 2017 1 2 2016 12 28 2017 5 17 5 24 PITCHf/x PITCHf/x PITCHf/x MLB 2014 PITCHf/x 1. 1 223 8522 3 14 1 2 223 8522 3 14 1 186 65 2 2017 PITCHf/x 1.1 PITCHf/x PITCHf/x SPORTVISION MLB 30
More informationECCS. ECCS,. ( 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file e
1 1 2015 4 6 1. ECCS. ECCS,. (https://ras.ecc.u-tokyo.ac.jp/guacamole/) 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file editor, Do View Do-file Editor Execute(do). 3. Mac System
More information1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,
More information2009 5 1...1 2...3 2.1...3 2.2...3 3...10 3.1...10 3.1.1...10 3.1.2... 11 3.2...14 3.2.1...14 3.2.2...16 3.3...18 3.4...19 3.4.1...19 3.4.2...20 3.4.3...21 4...24 4.1...24 4.2...24 4.3 WinBUGS...25 4.4...28
More information²¾ÁÛ¾õ¶·É¾²ÁË¡¤Î¤¿¤á¤Î¥Ñ¥Ã¥±¡¼¥¸DCchoice ¡Ê»ÃÄêÈÇ¡Ë
DCchoice ( ) R 2013 2013 11 30 DCchoice package R 2013/11/30 1 / 19 1 (CV) CV 2 DCchoice WTP 3 DCchoice package R 2013/11/30 2 / 19 (Contingent Valuation; CV) WTP CV WTP WTP 1 1989 2 DCchoice package R
More informationDirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :
Dirichlet Process : joint work with: Max Welling (UC Irvine), Yee Whye Teh (UCL, Gatsby) http://kenichi.kurihara.googlepages.com/miru_workshop.pdf 1 /40 MIRU2008 : Dirichlet process mixture Dirichlet process
More informationStata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(
mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual
More informationこんにちは由美子です
1 2 . sum Variable Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- var1 13.4923077.3545926.05 1.1 3 3 3 0.71 3 x 3 C 3 = 0.3579 2 1 0.71 2 x 0.29 x 3 C 2 = 0.4386
More information卒業論文
Y = ax 1 b1 X 2 b2...x k bk e u InY = Ina + b 1 InX 1 + b 2 InX 2 +...+ b k InX k + u X 1 Y b = ab 1 X 1 1 b 1 X 2 2...X bk k e u = b 1 (ax b1 1 X b2 2...X bk k e u ) / X 1 = b 1 Y / X 1 X 1 X 1 q YX1
More informationStata User Group Meeting in Kyoto / ( / ) Stata User Group Meeting in Kyoto / 21
Stata User Group Meeting in Kyoto / 2017 9 16 ( / ) Stata User Group Meeting in Kyoto 2017 9 16 1 / 21 Rosenbaum and Rubin (1983) logit/probit, ATE = E [Y 1 Y 0 ] ( / ) Stata User Group Meeting in Kyoto
More informationみっちりGLM
2015/3/27 12:00-13:00 日本草地学会若手 R 統計企画 ( 信州大学農学部 ) R と一般化線形モデル入門 山梨県富士山科学研究所 安田泰輔 謝辞 : 日本草地学会若手の会の皆様 発表の機会を頂き たいへんありがとうございます! 茨城大学 学生時代 自己紹介 ベータ二項分布を用いた種の空間分布の解析 所属 : 山梨県富士山科学研究所 最近の研究テーマ 近接リモートセンシングによる半自然草地のモニタリング手法開発
More information最小2乗法
2 2012 4 ( ) 2 2012 4 1 / 42 X Y Y = f (X ; Z) linear regression model X Y slope X 1 Y (X, Y ) 1 (X, Y ) ( ) 2 2012 4 2 / 42 1 β = β = β (4.2) = β 0 + β (4.3) ( ) 2 2012 4 3 / 42 = β 0 + β + (4.4) ( )
More information2 / 39
W707 s-taiji@is.titech.ac.jp 1 / 39 2 / 39 1 2 3 3 / 39 q f (x; α) = α j B j (x). j=1 min α R n+2 n ( d (Y i f (X i ; α)) 2 2 ) 2 f (x; α) + λ dx 2 dx. i=1 f B j 4 / 39 : q f (x) = α j B j (x). j=1 : x
More informationJOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alterna
JOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alternative approach using the Monte Carlo simulation to evaluate
More information報告書
1 2 3 4 5 6 7 or 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2.65 2.45 2.31 2.30 2.29 1.95 1.79 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 60 55 60 75 25 23 6064 65 60 1015
More information1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. 2 3 2 Conservative Depress. 3.1 2. SEM. 1. x SEM. Depress.
More informationX X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
More information浜松医科大学紀要
On the Statistical Bias Found in the Horse Racing Data (1) Akio NODA Mathematics Abstract: The purpose of the present paper is to report what type of statistical bias the author has found in the horse
More informationStata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim
TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls
More information分布
(normal distribution) 30 2 Skewed graph 1 2 (variance) s 2 = 1/(n-1) (xi x) 2 x = mean, s = variance (variance) (standard deviation) SD = SQR (var) or 8 8 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 8 0 1 8 (probability
More informationWASEDA University Internship Guide http://www.waseda.jp/career/internship/ 1 2 3 For International Students International students who are interested in internships with Japanese corporations must be
More information9 8 7 (x-1.0)*(x-1.0) *(x-1.0) (a) f(a) (b) f(a) Figure 1: f(a) a =1.0 (1) a 1.0 f(1.0)
E-mail: takio-kurita@aist.go.jp 1 ( ) CPU ( ) 2 1. a f(a) =(a 1.0) 2 (1) a ( ) 1(a) f(a) a (1) a f(a) a =2(a 1.0) (2) 2 0 a f(a) a =2(a 1.0) = 0 (3) 1 9 8 7 (x-1.0)*(x-1.0) 6 4 2.0*(x-1.0) 6 2 5 4 0 3-2
More informationわが国企業による資金調達方法の選択問題
* takeshi.shimatani@boj.or.jp ** kawai@ml.me.titech.ac.jp *** naohiko.baba@boj.or.jp No.05-J-3 2005 3 103-8660 30 No.05-J-3 2005 3 1990 * E-mailtakeshi.shimatani@boj.or.jp ** E-mailkawai@ml.me.titech.ac.jp
More informationp.1/22
p.1/22 & & & & Excel / p.2/22 & & & & Excel / p.2/22 ( ) ( ) p.3/22 ( ) ( ) Baldi Web p.3/22 ( ) ( ) Baldi Web ( ) ( ) ( p.3/22 ) Text Mining for Clementine True Teller Text Mining Studio Text Miner Trustia
More information第11回:線形回帰モデルのOLS推定
11 OLS 2018 7 13 1 / 45 1. 2. 3. 2 / 45 n 2 ((y 1, x 1 ), (y 2, x 2 ),, (y n, x n )) linear regression model y i = β 0 + β 1 x i + u i, E(u i x i ) = 0, E(u i u j x i ) = 0 (i j), V(u i x i ) = σ 2, i
More information03.Œk’ì
HRS KG NG-HRS NG-KG AIC Fama 1965 Mandelbrot Blattberg Gonedes t t Kariya, et. al. Nagahara ARCH EngleGARCH Bollerslev EGARCH Nelson GARCH Heynen, et. al. r n r n =σ n w n logσ n =α +βlogσ n 1 + v n w
More information2
NSCP-W61 08545-00U60 2 3 4 5 6 7 8 9 10 11 12 1 2 13 7 3 4 8 9 5 6 10 7 14 11 15 12 13 16 17 14 15 1 5 2 3 6 4 16 17 18 19 2 1 20 1 21 2 1 2 1 22 23 1 2 3 24 1 2 1 2 3 3 25 1 2 3 4 1 2 26 3 4 27 1 1 28
More informationMicrosoft PowerPoint - GLMMexample_ver pptx
Linear Mixed Model ( 以下 混合モデル ) の短い解説 この解説のPDFは http://www.lowtem.hokudai.ac.jp/plantecol/akihiro/sumida-index.html の お勉強 のページにあります. ver 20121121 と との間に次のような関係が見つかったとしよう 全体的な傾向に対する回帰直線を点線で示した ところが これらのデータは実は異なる
More informationIsogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,
H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x
More information,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,.,
J. of Population Problems. pp.,.,,,.,,..,,..,,,,.,.,,...,.,,..,.,,,. ,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,., ,,.,,..,,.,.,.,,,,,.,.,.,,,. European Labour Force Survey,,.,,,,,,,
More informationEvaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve Version: c 2003 Taku Terawaki, Akio Muranaka URL: http
14 9 27 2003 Evaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve 1 1 2 Version: 15 10 1 c 2003 Taku Terawaki, Akio Muranaka URL: http://www.taku-t.com/ 1 [14] 3 [10] 3 2 Andreoni[1] Duncan[7]
More information21世紀の統計科学 <Vol. III>
21 III HP, 2011 10 4 1 ( ), 1 tatsuya@e.u-tokyo.ac.jp 63 1 (Linear Mixed Model, LMM) (Best Linear Unbiased Predictor, BLUP) C.R. Henderson 50 LMM (Generalized Linear Mixed Model, GLMM) LMM LMM (Empirical
More informationudc-2.dvi
13 0.5 2 0.5 2 1 15 2001 16 2009 12 18 14 No.39, 2010 8 2009b 2009a Web Web Q&A 2006 2007a20082009 2007b200720082009 20072008 2009 2009 15 1 2 2 2.1 18 21 1 4 2 3 1(a) 1(b) 1(c) 1(d) 1) 18 16 17 21 10
More information09基礎分析講習会
データ解析の意味を理解しないでパソコンで計算して 序論 誤差解析 何のために も意味がない 以下の本でちゃんと勉強しよう R. A. Millikan ミリカン 水滴の蒸発 大学院生H. Fletcher 水滴を油滴に 博士論文単名 140の観測のうち49個除外 データ削除 実験データを正しく扱うために 化学同人編集部編 油滴実験 Regener がもともとThompsonの実験室(Cambridge
More informationバイオインフォマティクス特論12
藤 博幸 事後予測分布 パラメータの事後分布に従って モデルがどんなデータを期待するかを予測する 予測分布が観測されたデータと 致するかを確認することで モデルの適切さを確認できる 前回と同じ問題で事後予測を う 3-1-1. 個 差を考えない場合 3-1-2. 完全な個 差を考える場合 3-1-3. 構造化された個 差を考える場合 ベイズ統計で実践モデリング 10.1 個 差を考えない場合 第 10
More information推定モデル
2004 6 90 6 12 90 2000 2001 40 2001 131E-mail. kohara@osipp.osaka-u.ac.jp ** *** 1 1992 2.2% 99 4.7 2002 5.4% Machin and Manning (1999) Duration Dependence Turon (2003) Bover, Arellano and Betolila (2002)
More informationGLM PROC GLM y = Xβ + ε y X β ε ε σ 2 E[ε] = 0 var[ε] = σ 2 I σ 2 0 σ 2 =... 0 σ 2 σ 2 I ε σ 2 y E[y] =Xβ var[y] =σ 2 I PROC GLM
PROC MIXED ( ) An Introdunction to PROC MIXED Junji Kishimoto SAS Institute Japan / Keio Univ. SFC / Univ. of Tokyo e-mail address: jpnjak@jpn.sas.com PROC MIXED PROC GLM PROC MIXED,,,, 1 1.1 PROC MIXED
More informationPage 1 of 6 B (The World of Mathematics) November 20, 2006 Final Exam 2006 Division: ID#: Name: 1. p, q, r (Let p, q, r are propositions. ) (10pts) (a
Page 1 of 6 B (The World of Mathematics) November 0, 006 Final Exam 006 Division: ID#: Name: 1. p, q, r (Let p, q, r are propositions. ) (a) (Decide whether the following holds by completing the truth
More informationStudy on Application of the cos a Method to Neutron Stress Measurement Toshihiko SASAKI*3 and Yukio HIROSE Department of Materials Science and Enginee
Study on Application of the cos a Method to Neutron Stress Measurement Toshihiko SASAKI*3 and Yukio HIROSE Department of Materials Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa-shi,
More information1 1 1.1...................................... 1 1.2................................... 5 1.3................................... 7 1.4............................. 9 1.5....................................
More informationオーストラリア研究紀要 36号(P)☆/3.橋本
36 p.9 202010 Tourism Demand and the per capita GDP : Evidence from Australia Keiji Hashimoto Otemon Gakuin University Abstract Using Australian quarterly data1981: 2 2009: 4some time-series econometrics
More informationnenmatsu5c19_web.key
KL π ± e νe + e - (Ke3ee) Ke3ee ν e + e - Ke3 K 0 γ e + π - Ke3 KL ; 40.67(%) Ke3ee K 0 ν γ e + π - Ke3 KL ; 40.67(%) Me + e - 10 4 10 3 10 2 : MC Ke3γ : data K L real γ e detector matter e e 10 1 0 0.02
More information(lm) lm AIC 2 / 1
W707 s-taiji@is.titech.ac.jp 1 / 1 (lm) lm AIC 2 / 1 : y = β 1 x 1 + β 2 x 2 + + β d x d + β d+1 + ϵ (ϵ N(0, σ 2 )) y R: x R d : β i (i = 1,..., d):, β d+1 : ( ) (d = 1) y = β 1 x 1 + β 2 + ϵ (d > 1) y
More informationIPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
More information.. est table TwoSLS1 TwoSLS2 GMM het,b(%9.5f) se Variable TwoSLS1 TwoSLS2 GMM_het hi_empunion totchr
3,. Cameron and Trivedi (2010) Microeconometrics Using Stata, Revised Edition, Stata Press 6 Linear instrumentalvariables regression 9 Linear panel-data models: Extensions.. GMM xtabond., GMM(Generalized
More informationOn the Detectability of Earthquakes and Crustal Movements in and around the Tohoku District (Northeastern Honshu) (I) Microearthquakes Hiroshi Ismi an
On the Detectability of Earthquakes and Crustal Movements in and around the Tohoku District (Northeastern Honshu) (I) Microearthquakes Hiroshi Ismi and Akio TAKAGI Observation Center for Earthquake Prediction,
More informationPackageSoft/R-033U.tex (2018/March) R:
................................................................................ R: 2018 3 29................................................................................ R AI R https://cran.r-project.org/doc/contrib/manuals-jp/r-intro-170.jp.pdf
More information25 II :30 16:00 (1),. Do not open this problem booklet until the start of the examination is announced. (2) 3.. Answer the following 3 proble
25 II 25 2 6 13:30 16:00 (1),. Do not open this problem boolet until the start of the examination is announced. (2) 3.. Answer the following 3 problems. Use the designated answer sheet for each problem.
More information(interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2
7 2 1 (interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2 (confidence interval) 5 X a σ sqrt N µ X a σ sqrt N - 6 P ( X
More information(pdf) (cdf) Matlab χ ( ) F t
(, ) (univariate) (bivariate) (multi-variate) Matlab Octave Matlab Matlab/Octave --...............3. (pdf) (cdf)...3.4....4.5....4.6....7.7. Matlab...8.7.....9.7.. χ ( )...0.7.3.....7.4. F....7.5. t-...3.8....4.8.....4.8.....5.8.3....6.8.4....8.8.5....8.8.6....8.9....9.9.....9.9.....0.9.3....0.9.4.....9.5.....0....3
More informationMicrosoft PowerPoint - SSII_harada pptx
The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information
More informations = 1.15 (s = 1.07), R = 0.786, R = 0.679, DW =.03 5 Y = 0.3 (0.095) (.708) X, R = 0.786, R = 0.679, s = 1.07, DW =.03, t û Y = 0.3 (3.163) + 0
7 DW 7.1 DW u 1, u,, u (DW ) u u 1 = u 1, u,, u + + + - - - - + + - - - + + u 1, u,, u + - + - + - + - + u 1, u,, u u 1, u,, u u +1 = u 1, u,, u Y = α + βx + u, u = ρu 1 + ɛ, H 0 : ρ = 0, H 1 : ρ 0 ɛ 1,
More information2 1,2, , 2 ( ) (1) (2) (3) (4) Cameron and Trivedi(1998) , (1987) (1982) Agresti(2003)
3 1 1 1 2 1 2 1,2,3 1 0 50 3000, 2 ( ) 1 3 1 0 4 3 (1) (2) (3) (4) 1 1 1 2 3 Cameron and Trivedi(1998) 4 1974, (1987) (1982) Agresti(2003) 3 (1)-(4) AAA, AA+,A (1) (2) (3) (4) (5) (1)-(5) 1 2 5 3 5 (DI)
More informationelemmay09.pub
Elementary Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Activity Bank Number Challenge Time:
More information2
1 2 2005 15 17 21 22 24 25 67 95 3 1 2 3 4 17 4 5 6 7 8 9 PR PR PR 10 11 12 PR 419 844 1,490 950 590 20 12 50 13 12/20 2/28 3/30 14 17 349 666 15 59 6 11 15 17 14 15 15 17 3,525,992 15 59 15 17 18 910
More informationPari-gp /7/5 1 Pari-gp 3 pq
Pari-gp 3 2007/7/5 1 Pari-gp 3 pq 3 2007 7 5 Pari-gp 3 2007/7/5 2 1. pq 3 2. Pari-gp 3. p p 4. p Abel 5. 6. 7. Pari-gp 3 2007/7/5 3 pq 3 Pari-gp 3 2007/7/5 4 p q 1 (mod 9) p q 3 (3, 3) Abel 3 Pari-gp 3
More information2 値データの Intraclass Correlation Coefficient の推定マクロプログラム 稲葉洋介 1 田中紀子 1 1 国立国際医療研究センターデータサイエンス部生物統計研究室 Macro program for calculating Intraclass Correlati
2 値データの Intraclass Correlation Coefficient の推定マクロプログラム 稲葉洋介 1 田中紀子 1 1 国立国際医療研究センターデータサイエンス部生物統計研究室 Macro program for calculating Intraclass Correlation Coefficient for binary data Yosuke Inaba, Noriko
More informationJMP V4 による生存時間分析
V4 1 SAS 2000.11.18 4 ( ) (Survival Time) 1 (Event) Start of Study Start of Observation Died Died Died Lost End Time Censor Died Died Censor Died Time Start of Study End Start of Observation Censor
More information(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
More informationNLMIXED プロシジャを用いた生存時間解析 伊藤要二アストラゼネカ株式会社臨床統計 プログラミング グループグルプ Survival analysis using PROC NLMIXED Yohji Itoh Clinical Statistics & Programming Group, A
NLMIXED プロシジャを用いた生存時間解析 伊藤要二アストラゼネカ株式会社臨床統計 プログラミング グループグルプ Survival analysis using PROC NLMIXED Yohji Itoh Clinical Statistics & Programming Group, AstraZeneca KK 要旨 : NLMIXEDプロシジャの最尤推定の機能を用いて 指数分布 Weibull
More information3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N
RMT 1 1 1 N L Q=L/N (RMT), RMT,,,., Box-Muller, 3.,. Testing Randomness by Means of RMT Formula Xin Yang, 1 Ryota Itoi 1 and Mieko Tanaka-Yamawaki 1 Random matrix theory derives, at the limit of both dimension
More information