自由集会時系列part2web.key
|
|
- しのぶ あいしま
- 5 years ago
- Views:
Transcription
1 spurious correlation spurious regression
2
3 xt=xt-1+n(0,σ^2) yt=yt-1+n(0,σ^2)
4 n=20 type1error(5%)= no trend p for r
5 xt=xt-1+n(0,σ^2) random walk random walk variable variable time time i.i.d. normal variable xt=n(0,σ^2)
6 Granger & Newbold, 1974 Phillips, 1986
7
8
9 n=20 type1error(5%)= no trend histgram of p value for r resource output type error for r= p for r p (difference) p for r
10 n=20 type1error(5%)= no trend p for r
11 histgram of correlation coefficient r histgram of correlation coefficient r r r (difference)
12 n=20 type1error(5%)= no trend n=20 type1error(5%)= no trend p for r p for tau
13 sd=1and5 n=100 type1error= p value for r
14 type1error= unif vs unif type1error= unif vs normal p value for r p value for r
15 type1error= RW vs iid p value for r
16 sine curve 4cycles sine curve 2cycles sine curve 1 cycle variable variable variable time time time
17 sine curve 4cycles sine curve 2cycles sine curve 1 cycle type1error= RW vs sine4cycles type1error= RW vs sine2cycles type1error= RW vs sine1cycles variable variable variable time time time p value for r p value for r p value for r
18 sine curve quarter cycle type1error= RW vs sine1/4cycle variable time p value for r
19 xt=xt-1+n(0,σ^2) random walk random walk variable variable time time i.i.d. normal variable xt=n(0,σ^2)
20
21 n=20 n=10000 n=20 type1error(5%)= no trend n=10000 type1error(5%)= no trend p for r p for r
22 type1 error rate (5%) sample size (n)
23 n=10000 n=10000 type1error(5%)= no trend p for r
24 n=10000 n=10000 type1 error rate level of significance
25
26 Granger & Newbold, 1974 Phillips, 1986
27
28 xt=θx xt-1+n(0,σ^2) θx 1 θx 1
29
30 i.i.d. normal coef=0.95 coef=0.98 θx 0.00 θx 0.95 θx 0.98 variable variable variable time time time random walk coef=1.01 coef=1.02 θx 1.00 θx 1.01 θx 1.02 variable variable variable time time time
31 Granger et al (2001) Applied Economics, 33:
32 xt=θx xt-1+n(0,σ^2) θx 1 θx 1
33 θx=0.98 θx=0.95 type1error=0.674 theta=0.98 type1error= theta= p value for r p value for r θx=0.90 type1error= theta= p value for r
34 yt=α+β xt yt=α+β xt+εt εt 0
35 yt=α+β xt yt=α+β xt+εt εt 0
36 n=100 n=1000 Distribution of b n=100 RW Distribution of b n=1000 RW b b n=2000 Distribution of b n=2000 RW b
37 n=100 n=1000 Distribution of a n=100 RW Distribution of a n=1000 RW a n= Distribution of a n=2000 RW a
38 var(b) sample size (n) var(a) sample size (n)
39
40
41
42
43
44 resource output time series resource output time series y output (resource) output (resource) time time
45 resource output type error for r=0 resource output correlation p for r r r +1.0
1 911 9001030 9:00 A B C D E F G H I J K L M 1A0900 1B0900 1C0900 1D0900 1E0900 1F0900 1G0900 1H0900 1I0900 1J0900 1K0900 1L0900 1M0900 9:15 1A0915 1B0915 1C0915 1D0915 1E0915 1F0915 1G0915 1H0915 1I0915
More information10:30 12:00 P.G. vs vs vs 2
1 10:30 12:00 P.G. vs vs vs 2 LOGIT PROBIT TOBIT mean median mode CV 3 4 5 0.5 1000 6 45 7 P(A B) = P(A) + P(B) - P(A B) P(B A)=P(A B)/P(A) P(A B)=P(B A) P(A) P(A B) P(A) P(B A) P(B) P(A B) P(A) P(B) P(B
More informationuntitled
2 : n =1, 2,, 10000 0.5125 0.51 0.5075 0.505 0.5025 0.5 0.4975 0.495 0 2000 4000 6000 8000 10000 2 weak law of large numbers 1. X 1,X 2,,X n 2. µ = E(X i ),i=1, 2,,n 3. σi 2 = V (X i ) σ 2,i=1, 2,,n ɛ>0
More information1990年代以降の日本の経済変動
1990 * kenichi.sakura@boj.or.jp ** hitoshi.sasaki@boj.or.jp *** masahiro.higo@boj.or.jp No.05-J-10 2005 12 103-8660 30 * ** *** 1990 2005 12 1990 1990 1990 2005 11 2425 BIS E-mail: kenichi.sakura@boj.or.jp
More information研修コーナー
l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
More information_0212_68<5A66><4EBA><79D1>_<6821><4E86><FF08><30C8><30F3><30DC><306A><3057><FF09>.pdf
More information
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) = 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 informationuntitled
Horioka Nakagawa and Oshima u ( c ) t+ 1 E β (1 + r ) 1 = t i+ 1 u ( c ) t 0 β c t y t uc ( t ) E () t r t c E β t ct γ ( + r ) 1 0 t+ 1 1 = t+ 1 ξ ct + β ct γ c t + 1 1+ r ) E β t + 1 t ct (1
More information第13回:交差項を含む回帰・弾力性の推定
13 2018 7 27 1 / 31 1. 2. 2 / 31 y i = β 0 + β X x i + β Z z i + β XZ x i z i + u i, E(u i x i, z i ) = 0, E(u i u j x i, z i ) = 0 (i j), V(u i x i, z i ) = σ 2, i = 1, 2,, n x i z i 1 3 / 31 y i = β
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 information2 3
Sample 2 3 4 5 6 7 8 9 3 18 24 32 34 40 45 55 63 70 77 82 96 118 121 123 131 143 149 158 167 173 187 192 204 217 224 231 17 285 290 292 1 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
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 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 informationuntitled
3 3. (stochastic differential equations) { dx(t) =f(t, X)dt + G(t, X)dW (t), t [,T], (3.) X( )=X X(t) : [,T] R d (d ) f(t, X) : [,T] R d R d (drift term) G(t, X) : [,T] R d R d m (diffusion term) W (t)
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 informationII III II 1 III ( ) [2] [3] [1] 1 1:
2015 4 16 1. II III II 1 III () [2] [3] 2013 11 18 [1] 1 1: [5] [6] () [7] [1] [1] 1998 4 2008 8 2014 8 6 [1] [1] 2 3 4 5 2. 2.1. t Dt L DF t A t (2.1) A t = Dt L + Dt F (2.1) 3 2 1 2008 9 2008 8 2008
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 information物価変動の決定要因について ― 需給ギャップと物価変動の関係の国際比較を中心に―
NAIRU NAIRU NAIRU GDPGDP NAIRUNon- Accelerating Inflation Rate of Unemployment GDP GDP NAIRU Lown and RichFisher, Mahadeva and Whitley raw materials G NAIRUTurnerFai WatanabeNAIRU Watanabe nested NAIRU
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 informationchap10.dvi
. q {y j } I( ( L y j =Δy j = u j = C l ε j l = C(L ε j, {ε j } i.i.d.(,i q ( l= y O p ( {u j } q {C l } A l C l
More information28_04_斎藤正章.indd
37 2820103743,. 282010.3743 純 粋 持 株 会 社 における 経 営 管 理 上 の 課 題 1 2 ABSTR ACT...... 1 2 1 38 2 1 1 3 2 1 1 4 5 6 7 8 1 2 1 1992.269 39 1 1 1 2 9 1 2 3 123 13 2 1995.268 40,.1995 3 10, et al.1995 2 1950 2
More information医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 2 版 1 刷発行時のものです.
医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/009192 このサンプルページの内容は, 第 2 版 1 刷発行時のものです. i 2 t 1. 2. 3 2 3. 6 4. 7 5. n 2 ν 6. 2 7. 2003 ii 2 2013 10 iii 1987
More informationJA2008
A1 1 10 vs 3 2 1 3 2 0 3 2 10 2 0 0 2 1 0 3 A2 3 11 vs 0 4 4 0 0 0 0 0 3 6 0 1 4 x 11 A3 5 4 vs 5 6 5 1 0 0 3 0 4 6 0 0 1 0 4 5 A4 7 11 vs 2 8 8 2 0 0 0 0 2 7 2 7 0 2 x 11 A5 9 5 vs 3 10 9 4 0 1 0 0 5
More informationI L01( Wed) : Time-stamp: Wed 07:38 JST hig e, ( ) L01 I(2017) 1 / 19
I L01(2017-09-20 Wed) : Time-stamp: 2017-09-20 Wed 07:38 JST hig e, http://hig3.net ( ) L01 I(2017) 1 / 19 ? 1? 2? ( ) L01 I(2017) 2 / 19 ?,,.,., 1..,. 1,2,.,.,. ( ) L01 I(2017) 3 / 19 ? I. M (3 ) II,
More information3. みせかけの相関単位根系列が注目されるのは これを持つ変数同士の回帰には意味がないためだ 単位根系列で代表的なドリフト付きランダムウォークを発生させてそれを確かめてみよう yと xという変数名の系列をを作成する yt=0.5+yt-1+et xt=0.1+xt-1+et 初期値を y は 10
第 10 章 くさりのない犬 はじめにこの章では 単位根検定や 共和分検定を説明する データが単位根を持つ系列の場合 見せかけの相関をする場合があり 推計結果が信用できなくなる 経済分析の手順として 系列が定常系列か単位根を持つ非定常系列かを見極め 定常系列であればそのまま推計し 非定常系列であれば階差をとって推計するのが一般的である 1. ランダムウオーク 最も簡単な単位根を持つ系列としてランダムウオークがある
More informationこんにちは由美子です
Sample size power calculation Sample Size Estimation AZTPIAIDS AIDSAZT AIDSPI AIDSRNA AZTPr (S A ) = π A, PIPr (S B ) = π B AIDS (sampling)(inference) π A, π B π A - π B = 0.20 PI 20 20AZT, PI 10 6 8 HIV-RNA
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 information平成20年5月 協会創立50年の歩み 海の安全と環境保全を目指して 友國八郎 海上保安庁 長官 岩崎貞二 日本船主協会 会長 前川弘幸 JF全国漁業協同組合連合会 代表理事会長 服部郁弘 日本船長協会 会長 森本靖之 日本船舶機関士協会 会長 大内博文 航海訓練所 練習船船長 竹本孝弘 第二管区海上保安本部長 梅田宜弘
More information
読めば必ずわかる 分散分析の基礎 第2版
2 2003 12 5 ( ) ( ) 2 I 3 1 3 2 2? 6 3 11 4? 12 II 14 5 15 6 16 7 17 8 19 9 21 10 22 11 F 25 12 : 1 26 3 I 1 17 11 x 1, x 2,, x n x( ) x = 1 n n i=1 x i 12 (SD ) x 1, x 2,, x n s 2 s 2 = 1 n n (x i x)
More information1. 2. (Rowthorn, 2014) / 39 1
,, 43 ( ) 2015 7 18 ( ) E-mail: sasaki@econ.kyoto-u.ac.jp 1 / 39 1. 2. (Rowthorn, 2014) 3. 4. 5. 6. 7. 2 / 39 1 ( 1). ( 2). = +. 1. g. r. r > g ( 3).. 3 / 39 2 50% Figure I.1. Income inequality in the
More informationnsg02-13/ky045059301600033210
φ φ φ φ κ κ α α μ μ α α μ χ et al Neurosci. Res. Trpv J Physiol μ μ α α α β in vivo β β β β β β β β in vitro β γ μ δ μδ δ δ α θ α θ α In Biomechanics at Micro- and Nanoscale Levels, Volume I W W v W
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 information基礎数学I
I & II ii ii........... 22................. 25 12............... 28.................. 28.................... 31............. 32.................. 34 3 1 9.................... 1....................... 1............
More information#A A A F, F d F P + F P = d P F, F y P F F x A.1 ( α, 0), (α, 0) α > 0) (x, y) (x + α) 2 + y 2, (x α) 2 + y 2 d (x + α)2 + y 2 + (x α) 2 + y 2 =
#A A A. F, F d F P + F P = d P F, F P F F A. α, 0, α, 0 α > 0, + α +, α + d + α + + α + = d d F, F 0 < α < d + α + = d α + + α + = d d α + + α + d α + = d 4 4d α + = d 4 8d + 6 http://mth.cs.kitmi-it.c.jp/
More information: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
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, 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) 3, ,, , TOPIX, , explosive. 2,.,,,.,, 1
2016 1 12 4 1 2016 1 12, 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) 3, 1980 1990.,, 225 1986 4 1990 6, TOPIX,1986 5 1990 2, explosive. 2,.,,,.,, 1986 Q2 1990 Q2,,. :, explosive, recursiveadf,
More information日本統計学会誌, 第44巻, 第2号, 251頁-270頁
44, 2, 205 3 25 270 Multiple Comparison Procedures for Checking Differences among Sequence of Normal Means with Ordered Restriction Tsunehisa Imada Lee and Spurrier (995) Lee and Spurrier (995) (204) (2006)
More information(time series) ( 225 ) / / p.2/66
338 857 255 Tel : 48 858 3577, Fax : 48 858 3716 Email : tohru@ics.saitama-u.ac.jp URL : http://www.nls.ics.saitama-u.ac.jp/ tohru / / p.1/66 (time series) ( 225 ) / / p.2/66 / / p.3/66 ?? / / p.3/66 1.9.8.7.6???.5.4.3.2.1
More informationchap9.dvi
9 AR (i) (ii) MA (iii) (iv) (v) 9.1 2 1 AR 1 9.1.1 S S y j = (α i + β i j) D ij + η j, η j = ρ S η j S + ε j (j =1,,T) (1) i=1 {ε j } i.i.d(,σ 2 ) η j (j ) D ij j i S 1 S =1 D ij =1 S>1 S =4 (1) y j =
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 information19 σ = P/A o σ B Maximum tensile strength σ % 0.2% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional
19 σ = P/A o σ B Maximum tensile strength σ 0. 0.% 0.% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional limit ε p = 0.% ε e = σ 0. /E plastic strain ε = ε e
More informationuntitled
1 th 1 th Dec.2006 1 1 th 1 th Dec.2006 103 1 2 EITC 2 1 th 1 th Dec.2006 3 1 th 1 th Dec.2006 2006 4 1 th 1 th Dec.2006 5 1 th 1 th Dec.2006 2 6 1 th 1 th Dec.2006 7 1 th 1 th Dec.2006 3 8 1 th 1 th Dec.2006
More information> > <., vs. > x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D > 0 x (2) D = 0 x (3
13 2 13.0 2 ( ) ( ) 2 13.1 ( ) ax 2 + bx + c > 0 ( a, b, c ) ( ) 275 > > 2 2 13.3 x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D >
More information1 3 1.1.......................... 3 1............................... 3 1.3....................... 5 1.4.......................... 6 1.5........................ 7 8.1......................... 8..............................
More informationsolutionJIS.dvi
May 0, 006 6 morimune@econ.kyoto-u.ac.jp /9/005 (7 0/5/006 1 1.1 (a) (b) (c) c + c + + c = nc (x 1 x)+(x x)+ +(x n x) =(x 1 + x + + x n ) nx = nx nx =0 c(x 1 x)+c(x x)+ + c(x n x) =c (x i x) =0 y i (x
More informationseminar0220a.dvi
1 Hi-Stat 2 16 2 20 16:30-18:00 2 2 217 1 COE 4 COE RA E-MAIL: ged0104@srv.cc.hit-u.ac.jp 2004 2 25 S-PLUS S-PLUS S-PLUS S-code 2 [8] [8] [8] 1 2 ARFIMA(p, d, q) FI(d) φ(l)(1 L) d x t = θ(l)ε t ({ε t }
More informationuntitled
1 Hitomi s English Tests 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1 0 1 1 0 1 0 0 0 1 0 0 1 0 2 0 0 1 1 0 0 0 0 0 1 1 1 1 0 3 1 1 0 0 0 0 1 0 1 0 1 0 1 1 4 1 1 0 1 0 1 1 1 1 0 0 0 1 1 5 1 1 0 1 1 1 1 0 0 1 0
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 information画像工学特論
.? (x i, y i )? (x(t), y(t))? (x(t)) (X(ω)) Wiener-Khintchine 35/97 . : x(t) = X(ω)e jωt dω () π X(ω) = x(t)e jωt dt () X(ω) S(ω) = lim (3) ω S(ω)dω X(ω) : F of x : [X] [ = ] [x t] Power spectral density
More informationVol. 42 No pp Headcount ratio p p A B pp.29
1990 2003 2005 2000 1998 2004 2001 2 2000 2001 2000 1 Vol. 42 No. 2 2005 pp.21-22 25 25-29 30-34 1999 1 Headcount ratio 2 1995 20-25 25-30 2005 p.25 2005 2000 2 15 34 2003 p.3 15 34 A B 3 4 3 3 2003 pp.29-332001
More informationdpri04.dvi
47 B 6 4 Annuals of Disas. Prev. Res. Inst., Kyoto Univ., No. 47B, 24 AR AR :,, AR,. () point process Waymire et et al. (984) WGR () Over and Gupta (994, 996) Chatchai et al. (2) (23) Fig. Structure for
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 informationJKR Point loading of an elastic half-space 2 3 Pressure applied to a circular region Boussinesq, n =
JKR 17 9 15 1 Point loading of an elastic half-space Pressure applied to a circular region 4.1 Boussinesq, n = 1.............................. 4. Hertz, n = 1.................................. 6 4 Hertz
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 informationCG38.PDF
............3...3...6....6....8.....8.....4...9 3....9 3.... 3.3...4 3.4...36...39 4....39 4.....39 4.....4 4....49 4.....5 4.....57...64 5....64 5....66 5.3...68 5.4...7 5.5...77...8 6....8 6.....8 6.....83
More information01.Œk’ì/“²fi¡*
AIC AIC y n r n = logy n = logy n logy n ARCHEngle r n = σ n w n logσ n 2 = α + β w n 2 () r n = σ n w n logσ n 2 = α + β logσ n 2 + v n (2) w n r n logr n 2 = logσ n 2 + logw n 2 logσ n 2 = α +β logσ
More informationuntitled
MCMC 2004 23 1 I. MCMC 1. 2. 3. 4. MH 5. 6. MCMC 2 II. 1. 2. 3. 4. 5. 3 I. MCMC 1. 2. 3. 4. MH 5. 4 1. MCMC 5 2. A P (A) : P (A)=0.02 A B A B Pr B A) Pr B A c Pr B A)=0.8, Pr B A c =0.1 6 B A 7 8 A, :
More information基礎から学ぶトラヒック理論 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.
基礎から学ぶトラヒック理論 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/085221 このサンプルページの内容は, 初版 1 刷発行時のものです. i +α 3 1 2 4 5 1 2 ii 3 4 5 6 7 8 9 9.3 2014 6 iii 1 1 2 5 2.1 5 2.2 7
More informationkiyo5_1-masuzawa.indd
.pp. A Study on Wind Forecast using Self-Organizing Map FUJIMATSU Seiichiro, SUMI Yasuaki, UETA Takuya, KOBAYASHI Asuka, TSUKUTANI Takao, FUKUI Yutaka SOM SOM Elman SOM SOM Elman SOM Abstract : Now a small
More information商品流動性リスクの計量化に関する一考察(その2)―内生的流動性リスクを考慮したストレス・テスト―
E-mail: shigeru_yoshifuji@btm.co.jp E-mail: fuminobu_otake@btm.co.jp Bangia et al. G Bangia et al. exogenous liquidity risk endogenous liquidity risk et al LTCMLong Term Capital Management Fed G G T
More information投資家の株式需要関数におけるボラティリティの限界効果と構造変化
Fama Semi-strong form BIS TOPIX Kamesaka, Nofsinger and Kawakita Choe, Kho and Stulz Grinblatt and Keloharju Kang and Stulz Choe et al. Dahlquist and Robertsson Kalev, Nguyen and Oh Froot and Ramadorai
More informationtokei01.dvi
2. :,,,. :.... Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 3. (probability),, 1. : : n, α A, A a/n. :, p, p Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN
More information研究シリーズ第40号
165 PEN WPI CPI WAGE IIP Feige and Pearce 166 167 168 169 Vector Autoregression n (z) z z p p p zt = φ1zt 1 + φ2zt 2 + + φ pzt p + t Cov( 0 ε t, ε t j )= Σ for for j 0 j = 0 Cov( ε t, zt j ) = 0 j = >
More informationσ t σ t σt nikkei HP nikkei4csv H R nikkei4<-readcsv("h:=y=ynikkei4csv",header=t) (1) nikkei header=t nikkei4csv 4 4 nikkei nikkei4<-dataframe(n
R 1 R R R tseries fseries 1 tseries fseries R Japan(Tokyo) R library(tseries) library(fseries) 2 t r t t 1 Ω t 1 E[r t Ω t 1 ] ɛ t r t = E[r t Ω t 1 ] + ɛ t ɛ t 2 iid (independently, identically distributed)
More informationInternational Classification of Diseases (ICD) について :[3][4] Standard diagnostic tool for epidemiology, health management and clinical purposes. This i
季節性インフルエンザの疾病負荷推定 Estimating the disease burden associated with seasonal influenza in Japan 東京大学大学院 総合文化研究科水本憲治 Kenji Mizumoto Graduate School of Arts and Sciences, The University of Tokyo はじめに インフルエンザは毎年流行し
More information