Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
statistician_ja_lt5.pdf
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Takayuki Uchiba
November 15, 2020
Science
740
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
statistician_ja_lt5.pdf
一様最小分散不偏推定量が存在しない例を紹介しました。
Takayuki Uchiba
November 15, 2020
More Decks by Takayuki Uchiba
See All by Takayuki Uchiba
縮小推定のはなし.pdf
utaka233
1
2.8k
高次元データに対するL1正則化の有効性
utaka233
1
3.3k
Other Decks in Science
See All in Science
(CVPR2026) Back to Basics: Let Denoising Generative Models Denoise
shumpei777
0
320
Tensor Factorization Meets Deformed Information Geometry: Convex Relaxation under Deformed Algebra
gkazunii
0
140
20260722【JAWS-UG東京 ランチタイムLT会 #37④】AWS Well-Architectedフレームワークに沿った回答をするAIエージェントを作ってみた
nozakijcom
1
140
ssmonline #51 ヤマサキ春のサメ祭り 2026 / ssmjp Yamasaki Spring JAWS Festival 2026
naospon
1
140
Kritische evaluatie van GenAI-output voor literatuuronderzoek
voginip
0
230
データベース09: 実体関連モデル上の一貫性制約
trycycle
PRO
0
1.8k
Conwayの法則を"ちゃんと"使うために — 原典でConwayは何を言っていたのか
bonotake
10
6.8k
データベース01: データベースを使わない世界
trycycle
PRO
1
1.5k
From Prediction to Understanding: Causal Discovery for Data Science and AI Applications
sshimizu2006
0
270
生成AI・プレプリント時代における 研究成果公開の再設計 ― トップカンファレンス文化はどこへ向かうのか / Redesigning the Dissemination of Research Outputs in the Age of Generative AI and Preprints — Where Is the Top-Conference Culture Heading?
ykiyota
0
30k
J-STAGE全文XML登載必須化について
xspa2012
0
1.5k
不動産業界における業界特化のデータ整備とAI活用 ─Vertical DataとVertical AI─
estie
1
940
Featured
See All Featured
The Straight Up "How To Draw Better" Workshop
denniskardys
239
140k
It's Worth the Effort
3n
188
29k
Paper Plane (Part 1)
katiecoart
PRO
1
10k
Building the Perfect Custom Keyboard
takai
2
860
Bioeconomy Workshop: Dr. Julius Ecuru, Opportunities for a Bioeconomy in West Africa
akademiya2063
PRO
1
320
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
The Pragmatic Product Professional
lauravandoore
37
7.4k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
2.2k
The Illustrated Guide to Node.js - THAT Conference 2024
reverentgeek
1
460
Sam Torres - BigQuery for SEOs
techseoconnect
PRO
0
510
Statistics for Hackers
jakevdp
799
230k
Transcript
Ұ༷࠷খࢄෆภਪఆྔඞͣଘࡏ͢Δ͔ʁ !VUBLB
Ұ༷࠷খࢄෆภਪఆྔ ͷҰ༷࠷খࢄෆภਪఆྔʢ6.76&ʣɹ ෆภੑ ͕ͲΜͳͰ͋ͬͯɺ ͕Γཱͭɻ Ұ༷࠷খࢄੑ ͕ͲΜͳͰ͋ͬͯɺଞͷෆภਪఆྔ
ʹൺͯ ͷࢄ͕খ͍͞ɻཁ͢Δʹɺ ͕Γཱͭɻ ྫαΠζ ͷಠཱඪຊΛظ ࢄ ͷਖ਼ن͔Βಘͨ߹ ɾظ ͷ6.76&ඪຊฏۉ ɾࢄ ͷ6.76&ෆภࢄ T θ θ [T] = θ θ S T [T] ≤ [S] n μ σ2 μ σ2
6.76&ͷѻ͍͢͞ʢͦͷʣ $SBNFS3BPͷఆཧ͋Δෆภਪఆྔ͕6.76&͔ఆ͢Δํ๏ͷͻͱͭ ɾෆภਪఆྔͷࢄͷେ͖͞ͷԼݶܭࢉͰ͖Δɻ ɹɾ$SBNFS3BPԼݶ'JTIFSใྔ ɾෆภਪఆྔ ͷࢄ͕͜ͷԼݶʹҰக͢Ε6.76& ʢʣ6.76&ͷࢄ͕ඞͣ͜ͷԼݶʹͳΔΘ͚Ͱͳ͍ɻ T
6.76&ͷѻ͍͢͞ʢͦͷʣ -FINBOO4DIF⒎Fͷఆཧ6.76&ಛఆͷ݅ͷͱͰ࡞ΕΔɻ ɾಛఆͷ݅උे౷ܭྔͷଘࡏ ɾඋे౷ܭྔͰද͞ΕΔ౷ܭྔ͕ෆภͳΒ6.76& ɾ$SBNFS3BPͷఆཧ͕༗ޮͰͳ͍έʔεͰಛʹศར ɹɾ'JTIFSใྔ͕ఆٛͰ͖ͳ͍ͱ͖ʢҰ༷ͷ࠷େύϥϝʔλʣ ɹɾ6.76&ͷࢄ㱠$SBNFS3BPԼݶͷͱ͖
6.76&ඞͣଘࡏ͢Δ͔ʁ ύϥϝʔλ ͷ6.76&ඞͣଘࡏ͢Δ͔ʁ ɾ-FINBOO4DIF⒎Fͷఆཧඋे౷ܭྔ͕ଘࡏ͢Ε6.76&࡞ΕΔɻ ɾඋे౷ܭྔ͕ଘࡏ͠ͳ͚ΕͲ͏͔ʁ ɾ)JOUҰ༷࠷খࢄੑʹݱΕΔʮ ͕ͲΜͳͰ͋ͬͯʯڧ͍݅ ˠɹ ͷ͝ͱʹ࠷খࢄͷෆภਪఆྔ͕ҟͳΕɺ6.76&ଘࡏ͠ͳ͍ɻ θ
θ θ
۩ମྫͷߏ ֬ม ͕࣍ͷ࣭֬ྔؔ ʹै͍ͬͯΔͷͱ͠·͢ɻύϥϝʔλ ͷҰ༷࠷খࢄෆภਪఆྔଘࡏ͢Δ͔ʁ ٕज़తͳ3FNBSL Ͱද͞ΕΔͲΜͳ౷ܭྔɺ ͷܗͰද͢͜ͱ͕Ͱ͖Δɻ X
f(x) = { p JGx = − 1 (1 − p)2px JGx = 0,1,2,⋯ p X T(X) = ∞ ∑ x=−1 tx [X = x]
ෆภਪఆྔʹͳΔͨΊͷ݅ Λ༻͍ͯɺ౷ܭྔ ͷظΛܭࢉ͢Δɻ ౷ܭྔ ͕ෆภਪఆྔͳΒɺظඞͣ ʹ͘͠ͳΔɻ ˠɹԽࣜ GPS
ˠɹ ͷܗͰද͞ΕΔ౷ܭྔ ͕ෆภਪఆྔʹͳΔɻ f(x) = { p JGx = − 1 (1 − p)2px JGx = 0,1,2,⋯ T(X) [T] = t−1 p + ∞ ∑ x=0 tx (1 − p)2px = t0 + ∞ ∑ x=1 (tx−2 − 2tx−1 + tx )px T(X) p tx − 2tx−1 + tx−2 = 0 x ≥ 2 t1 = 1 − t−1 t0 = 0 tx = x(1 − t−1 ) T(X)
ෆภਪఆྔͷࢄΛܭࢉ͢Δʢͦͷʣ Λ༻͍ͯɺෆภਪఆྔ ͷࢄΛܭࢉ͢Δɻ ͜͏͍͏ͱ͖ʹཱͭͷࢄͷެࣜʂ ͳͷͰɺ Λܭࢉ͠Α͏ɻ f(x) = {
p JGx = − 1 (1 − p)2px JGx = 0,1,2,⋯ T(X) [T] = [T2] − [T]2 = [T2] − p2, ෆภੑ [T2]
ෆภਪఆྔͷࢄΛܭࢉ͢Δʢͦͷʣ Ώ͑ʹɺ ͷࢄ ͱΘ͔Γ·͢ɻ [T2] = t2 −1 p
+ ∞ ∑ x=1 x2(1 − t−1 )2(1 − p)2px = t−1 + (1 − t−1 )2(1 − p)2 ∞ ∑ x=1 x2px = t2 −1 p + (1 − t−1 )2(1 − p)2 p(1 + p) (1 − p)3 = t2 −1 p + (1 − t−1 )2 p(1 + p) 1 − p T(X) [T] = t2 −1 p + (1 − t−1 )2 p(1 + p) 1 − p − p2
ࢄ͕࠷খΛͱΔͨΊͷ݅ʢͦͷʣ ࠷খࢄΛ༩͑Δ ʢΛ༩͑Δ ʣΛٻΊɺ ʹґଘͳΒ6.76&ଘࡏ͠ͳ͍ɻ Λ Ͱཧ͢Δͱɺ͕࣍ؔݱΕΔɻ ฏํͯ͠ɺ࠷খΛ༩͑Δ ΛٻΊͯΈΑ͏ʂ
T(X) t−1 p [T] = t2 −1 p + (1 − t−1 )2 p(1 + p) 1 − p − p2 t−1 [T] = (p + p(1 + p) 1 − p ) t2 −1 − 2 p(1 + p) 1 − p t−1 + ( p(1 + p) 1 − p − p2 ) t−1
ࢄ͕࠷খΛͱΔͨΊͷ݅ʢͦͷʣ ฏํ͢Δͱɺ࣍ͷΑ͏ʹͳΓ·͢ɻ ࣍ؔͷͷ࠲ඪΛಡΉ͜ͱͰɺ ͷͱ͖ࢄ࠷খͱΘ͔Γ·͢ɻ [T] = (p + p(1
+ p) 1 − p ) t−1 − 1 1 + 1 − p 1 + p 2 + const . = (p + p(1 + p) 1 − p ) {t−1 − p + 1 2 } 2 + const . t−1 = p + 1 2
݁ ࢄ͕࠷খʹͳΔෆภਪఆྔ͕ύϥϝʔλ ͷʹґଘ͍ͯ͠Δɻ ˠɹ ͷ6.76&ଘࡏ͠ͳ͍ʂʂʂʂʂ ͜ͷྫ͕ڭ͑ͯ͘Ε͍ͯΔͱࢥ͏͜ͱʢࢲײʣ ɾඞͣ͠ʮ͍ͭͰ҆ఆͯ͠ਫ਼͕ྑ͍ਪఆྔʯ͕ଘࡏ͢ΔͱݶΒͳ͍ɻ ɾԾઆ͕͋ΔͳΒਪఆྔʹөͤͯ͞ΈΔͷେࣄɻ ɹɾࠓճͷྫͰɺ ͷʹԠͨ͡ਪఆྔͷબͷ༨͞Ε͍ͯΔɻ
ɹɾDMJDLখ͘͞ͳΓ͕͔ͪͩΒɺͪΐͬͱॖখͨ͠ͷΛ͓͏ͱ͔ɻ p p p
͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠ʂ ࣗݾհͰͬͱ͖·͢ɻɻɻ ɾ!VUBLB ɾגࣜձࣾ͢͏͕͘ͿΜ͔ ڭ෦ ෦ ɾڵຯཧ౷ܭֶͷσʔλϚΠχϯάͷԠ༻ زԿֶ ɾจ ɹओஶ(MVJOH4UBCJMJUZ$POEJUJPOTPO3VMFE4VSGBDFXJUI1PTJUJWF(FOVT
ɹɹɹɹ0TBLB+PVSOBMPG.BUIFNBUJDT BDDFQUFE ɹڞஶࠓຊɺ͍ͣΕػցֶशͷจɻ