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research.qmd
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---
title: "μ°κ΅¬ μμΉ΄μ΄λΈ"
page-layout: full
comments: false
---
λ
Όλ¬Έκ³Ό μ±
, μΉμ¬μ΄νΈ λ±μ ν΅ν΄ 곡λΆνκ³ μ°κ΅¬ν κ²λ€μ μμΉ΄μ΄λΈν©λλ€.
μ°Έκ³ λ¬Ένκ³Ό μ€ν°λ λ
ΈνΈ, κ·Έλ¦¬κ³ μ¬νκ°λ₯ν μμ€μ½λλ₯Ό ν¨κ» μ 곡νκ³ μ ν©λλ€.
## Experimentation
- Bao, W. (2023, March 28). How to Size For Online Experiments With Ratio Metrics. Expedia Group Technology. https://medium.com/expedia-group-tech/how-to-size-for-online-experiments-with-ratio-metrics-3d57362f1967
- Blocker, C., Conway, J., Demortier, L., Heinrich, J., Junk, T., Lyons, L., & Punzi, G. (2006). Simple Facts about P -Values.
- Deng, A., Lu, J., & Litz, J. (2017). Trustworthy Analysis of Online A/B Tests: Pitfalls, challenges and solutions. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 641β649. https://doi.org/10.1145/3018661.3018677
- Fabijan, A., Dmitriev, P., Arai, B., Drake, A., Kohlmeier, S., & Kwong, A. (2023). A/B Integrations: 7 Lessons Learned from Enabling A/B testing as a Product Feature. 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 304β314. https://doi.org/10.1109/ICSE-SEIP58684.2023.00033
- Gupta, S., Kohavi, R., Tang, D., Xu, Y., Andersen, R., Bakshy, E., Cardin, N., Chandran, S., Chen, N., Coey, D., Curtis, M., Deng, A., Duan, W., Forbes, P., Frasca, B., Guy, T., Imbens, G. W., Saint Jacques, G., Kantawala, P., β¦ Yashkov, I. (2019). Top Challenges from the first Practical Online Controlled Experiments Summit. ACM SIGKDD Explorations Newsletter, 21(1), 20β35. https://doi.org/10.1145/3331651.3331655
- Gupta, S., Ulanova, L., Bhardwaj, S., Dmitriev, P., Raff, P., & Fabijan, A. (2018). The Anatomy of a Large-Scale Experimentation Platform. 2018 IEEE International Conference on Software Architecture (ICSA), 1β109. https://doi.org/10.1109/ICSA.2018.00009
Huang, C., Tang, Y., & Tang, C. H. and Y. (2022, May 24). Meet Dash-AB β The Statistics Engine of Experimentation at DoorDash. DoorDash Engineering Blog. https://doordash.engineering/2022/05/24/meet-dash-ab-the-statistics-engine-of-experimentation-at-doordash/
- Kohavi, R. (2023, October). Trustworthy A/B Tests: Causality and Pitfalls. Google Docs. https://drive.google.com/file/d/1mbwrCkR52kIfcjkQibI4lRMwOSJJjdv6/view?usp=sharing&usp=embed_facebook
- Kohavi, R., Deng, A., Frasca, B., Longbotham, R., Walker, T., & Xu, Y. (2012). Trustworthy online controlled experiments: Five puzzling outcomes explained. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 786β794. https://doi.org/10.1145/2339530.2339653
- Kohavi, R., Deng, A., & Vermeer, L. (2022). A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3168β3177. https://doi.org/10.1145/3534678.3539160
- Kohavi, R., & Longbotham, R. (2011). Unexpected results in online controlled experiments. ACM SIGKDD Explorations Newsletter, 12(2), 31β35. https://doi.org/10.1145/1964897.1964905
- Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. https://experimentguide.com/
- Kohavi, R., Tang, D., Xu, Y., Hemkens, L. G., & Ioannidis, J. P. A. (2020). Online randomized controlled experiments at scale: Lessons and extensions to medicine. Trials, 21(1), 150. https://doi.org/10.1186/s13063-020-4084-y
Machmouchi, W., Gupta, S., Zhang, R., & Fabijan, A. (2020, July 31). Patterns of Trustworthy Experimentation: Pre-Experiment Stage. Microsoft Research. https://www.microsoft.com/en-us/research/group/experimentation-platform-exp/articles/patterns-of-trustworthy-experimentation-pre-experiment-stage/
- Microsoft. (2021, January 25). Patterns of Trustworthy Experimentation: During-Experiment Stage. Microsoft Research. https://www.microsoft.com/en-us/research/group/experimentation-platform-exp/articles/patterns-of-trustworthy-experimentation-during-experiment-stage/
- Schultzberg, M., Kjellin, O., & Rydberg, J. (2020). Statistical Properties of Exclusive and Non-exclusive Online Randomized Experiments using Bucket Reuse (arXiv:2012.10202). arXiv. https://doi.org/10.48550/arXiv.2012.10202
- Schultzberg, M., Kjellin, O., & Rydberg, J. (2021, March 10). Spotifyβs New Experimentation Coordination Strategy. Spotify Engineering. https://engineering.atspotify.com/2021/03/spotifys-new-experimentation-coordination-strategy/
- Thumbtack, E. (2020, June 1). SeedfinderβInfrastructure to Improve Sample Balance in Online A/B Tests. Thumbtack Engineering. https://medium.com/thumbtack-engineering/seedfinder-infrastructure-to-improve-sample-balance-in-online-a-b-tests-1b8c3ae7dbe8
- Xie, H., & Aurisset, J. (2016). Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 645β654. https://doi.org/10.1145/2939672.2939733
## Time Series
### 1 μΆλ‘ λͺ¨λΈλ§ Β· Regression
#### Spurious regression
- λμ’
ν. R μμ© μκ³μ΄λΆμ. μμ μμΉ΄λ°λ―Έ. 2020.
- μ¬λ¬ μκ³μ΄λ‘ νκ·λ₯Ό μνν λ, κΌ μ£Όμν΄μΌ ν μμλμ΄μΌν μ¬ν
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/76?category=1019644)
- π [R νν 리μΌ](https://be-favorite.github.io/Multiple_timeseries/CCF%20analysis%20and%20DLM/Tutorials_DLM.html): CCF λΆμμ νꡬμ μκ΄ νμΈ κ³Όμ μ°Έκ³
#### Regression with ARIMA errors
- λμ’
ν. R μμ© μκ³μ΄λΆμ. μμ μμΉ΄λ°λ―Έ. 2020.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/74?category=1019644)
- π [R νν 리μΌ](https://be-favorite.github.io/Multiple_timeseries/CCF%20analysis%20and%20DLM/Tutorials_DLM.html)
#### Distributed lag model
- λμ’
ν. R μμ© μκ³μ΄λΆμ. μμ μμΉ΄λ°λ―Έ. 2020.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/75?category=1019644)
#### Distributed lag non-linear model
- Gasparrini, Antonio, Benedict Armstrong, and M.G. Kenward. "Distributed Lag Non-Linear Models." Statistics in Medicine 29 (September 20, 2010): 2224--34. https://doi.org/10.1002/sim.3940.
- Gasparrini, Antonio. "Distributed Lag Linear and Non-Linear Models in R: The Package Dlnm." Journal of Statistical Software 43 (July 1, 2011): 1--20. https://doi.org/10.18637/jss.v043.i08.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/80)
- π [PPT](https://be-favorite.github.io/Presentation_archive/DLM%2C%20DLNM/Introduction_dlm%2Cdlnm.html#6)
- π [R νν 리μΌ](https://be-favorite.github.io/Multiple_timeseries/DLNMs/Tutorials_DLNMs.html)
### 2 μμΈ‘λͺ¨λΈλ§ Β· Forecasting
#### Exponential Smoothing
- λμ’
ν. R μμ© μκ³μ΄λΆμ. μμ μμΉ΄λ°λ―Έ. 2020.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/62?category=928223)
- π [R νν 리μΌ: tidyverse principleλ‘ μκ³μ΄ μλ£λΆμνκΈ°](https://www.taemobang.com/posts/2022-03-11-do-time-series-analysis-with-tidyverse-principle/)
#### ARIMA model
- λμ’
ν. R μμ© μκ³μ΄λΆμ. μμ μμΉ΄λ°λ―Έ. 2020.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/63?category=928223)
#### Prophet
- Taylor, Sean, and Benjamin Letham. Forecasting at Scale, 2017. https://doi.org/10.7287/peerj.preprints.3190v2.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/64)
- π [R νν 리μΌ](https://be-favorite.github.io/Tutorial_prophet/Report.html)
#### Hierarchical Time Series Forecasting
- Athanasopoulos, George, Roman A. Ahmed, and Rob J. Hyndman. "Hierarchical Forecasts for Australian Domestic Tourism." International Journal of Forecasting 25, no. 1 (January 1, 2009): 146--66. https://doi.org/10.1016/j.ijforecast.2008.07.004.
- Athanasopoulos, George, Rob Hyndman, Roman Ahmed, and Han Lin Shang. "Optimal Combination Forecasts for Hierarchical." Computational Statistics & Data Analysis 55 (September 1, 2011): 2579--89. https://doi.org/10.1016/j.csda.2011.03.006.
- Hyndman, Rob J, George Athanasopoulos, and Han Lin Shang. "Hts: An R Package for Forecasting Hierarchical or Grouped Time Series," n.d., 12.
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/60?category=928223)
- π [R νν 리μΌ](https://otexts.com/fpp3/hts.html)
### 3 Other techniques
#### Intervention analysis (Interrupted Time Series)
- Slides. "Intervention Analysis." Accessed April 17, 2022. https://slides.com/tonyg/intervention-analysis.
- π [μ°Έκ³ μλ£](https://be-favorite.github.io/Blog/resources/research/intervention_analysis/ITS_source.pdf)
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.github.io/Blog/resources/research/intervention_analysis/ITS_note.pdf)
- π [R μ½λ](https://be-favorite.github.io/Blog/resources/research/intervention_analysis/ITS_rcode.R)
- π [R μ½λ: arimax() νν 리μΌ](https://be-favorite.github.io/Blog/resources/research/intervention_analysis/ITS_arimax()_rcode.R)
#### Dynamic Time Warping (DTW)
- Berndt, Donald J., and James Clifford. "Using Dynamic Time Warping to Find Patterns in Time Series." In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, 359--70. AAAIWS'94. Seattle, WA: AAAI Press, 1994.
- μ ν λλ νννλ μκ³μ΄, μμ°¨κ° μ‘΄μ¬νλ μ μ¬ν ν¨ν΄μ΄ μ‘΄μ¬νλ λ μκ³μ΄μ μ‘μλΌ μ μκ²λ ν΄μ£Όλ λΉμ μ¬μ± μΈ‘λ(거리 μΈ‘λ) μκ³ λ¦¬μ¦
- DTW distanceλ₯Ό μ΄μ©ν΄ κ³μΈ΅μ κ΅°μ§ λΆμ μν κ°λ₯
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.github.io/Blog/resources/research/dtw/DTW_note.pdf)
- π [R νν 리μΌ](https://be-favorite.github.io/Blog/resources/research/dtw/DTW_tutorial.pdf)
#### Discrete Wavelet Transform (DWT)
- Graps, Amara. "An Introduction to Wavelets." IEEE Comp. Sci. Engi. 2 (February 1, 1995): 50--61. https://doi.org/10.1109/99.388960.
- Li, Daoyuan, TegawendΓ© F. BissyandΓ©, Jacques Klein, and Y. L. Traon. "Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study." In SEKE, 2016. https://doi.org/10.18293/SEKE2016-067.
- μκ³μ΄λ€μ λ°μ΄ν°μ μ΄λ‘ λμ΄νμ¬ classificationμ μνν λ, ν¨κ³Όμ μΈ μ°¨μ κ°μ λ°©λ²
- μΌμ’
μ μκ³μ΄ Feature engineering κΈ°λ²μ ν΄λΉ
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.github.io/Blog/resources/research/dwt/DWT_note.pdf)
- π [R νν 리μΌ](https://be-favorite.github.io/Blog/resources/research/dwt/DWT_tutorial.pdf)
## Statistical/Machine Learning
### Prerequisite
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 1 λ¨Έμ λ¬λ μ©μ΄ μ 리](https://be-favorite.tistory.com/30?category=894492)
### Ensemble methods
- Chen, Tianqi, and Carlos Guestrin. "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13, 2016, 785--94. https://doi.org/10.1145/2939672.2939785.
- Chen, Lilly. "Basic Ensemble Learning (Random Forest, AdaBoost, Gradient Boosting)- Step by Step Explained." Medium, January 2, 2019. https://towardsdatascience.com/basic-ensemble-learning-random-forest-adaboost-gradient-boosting-step-by-step-explained-95d49d1e2725.
- Morde, Vishal. "XGBoost Algorithm: Long May She Reign!" Medium, April 8, 2019. https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d.
- "Light GBM vs XGBOOST: Which Algorithm Takes the Crown." Accessed April 17, 2022. https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/.
- **Random Forest**, **AdaBoost**, **Gradient Boosting**, **XGBoost**, **Light GBM**
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/2)
- π [R νν 리μΌ: tidyverse principleλ‘ λ¨Έμ λ¬λνκΈ°](https://www.taemobang.com/posts/2022-04-04-do-machine-learning-with-tidyverse-principle/)
### Logistic regression
- James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "An Introduction to Statistical Learning." An Introduction to Statistical Learning. Accessed April 17, 2022. https://www.statlearning.com.
- Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd ed. Springer, 2009. http://www-stat.stanford.edu/\~tibs/ElemStatLearn/.
- StatQuest with Josh Starmer. Logistic Regression Details Pt 2: Maximum Likelihood, 2018. https://www.youtube.com/watch?v=BfKanl1aSG0.
- Chatterjee, Samprit, and Ali S. Hadi. "Regression Analysis by Example, Fifth Edition."
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/47)
### Generalized Linear Model (GLM) and Generalized Additive Model (GAM)
- Hayes, Genevieve. "Beyond Linear Regression: An Introduction to GLMs." Medium, December 24, 2019. https://towardsdatascience.com/beyond-linear-regression-an-introduction-to-glms-7ae64a8fad9c.
- James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "An Introduction to Statistical Learning." An Introduction to Statistical Learning. Accessed April 17, 2022. https://www.statlearning.com.
- **GLM**
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/52)
- **GAM**
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 1 μ νλͺ¨νμ νκ³](https://be-favorite.tistory.com/53?category=923110)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 2 λ€ν νκ·μ κ³λ¨ ν¨μ](https://be-favorite.tistory.com/54?category=923110)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 3 Regression splines](https://be-favorite.tistory.com/56?category=923110)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 4 Smoothing splines](https://be-favorite.tistory.com/57?category=923110)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 5 Local regressions](https://be-favorite.tistory.com/58?category=923110)
- π [μ€ν°λ λ
ΈνΈ: GAMs](https://be-favorite.tistory.com/59?category=923110)
## Deep Learning
### Prerequisites
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 1 λ₯λ¬λμ λͺ¨ν°λ² μ΄μ
κ³Ό μμ¬](https://be-favorite.tistory.com/8?category=897337)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 2 μ νλμμ μ¬λ¬ κ°μ²΄ μκ°](https://be-favorite.tistory.com/33?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 3 νλ ¬μ μ μΉμ λΈλ‘λμΊμ€ν
](https://be-favorite.tistory.com/32?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 4 νλ ¬κ³Ό 벑ν°μ κ³±μ°μ°](https://be-favorite.tistory.com/34?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 5 μ νλ°©μ μκ³Ό μ νμ’
μ,span](https://be-favorite.tistory.com/35?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 6 norms](https://be-favorite.tistory.com/36?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 7 νΉλ³ν μ’
λ₯μ νλ ¬κ³Ό 벑ν°](https://be-favorite.tistory.com/37?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 8 κ³ μ³κ° λΆν΄](https://be-favorite.tistory.com/38?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 9 νΉμκ° λΆν΄μ μΌλ°ν μνλ ¬](https://be-favorite.tistory.com/39?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 10 Trace μ°μ°μμ νλ ¬μ](https://be-favorite.tistory.com/40?category=909652)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 11 μ νλμλ₯Ό μ΄μ©ν μ£Όμ±λΆ μ λ](https://be-favorite.tistory.com/41)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 12 λ¨Έμ λ¬λ μ©μ΄ μ 리](https://be-favorite.tistory.com/30?category=894492)
## High-Dimensional Data Analysis
- Breheny, Patrick. High-Dimensional Data Analysis. The University of Iowa, 2016. https://myweb.uiowa.edu/pbreheny/7600/s16/index.html.
- π [R μμ€μ½λ λ° μμ Dataset μ 곡](https://myweb.uiowa.edu/pbreheny/7600/s16/index.html)
- μΌλ°μ μΈ κΈ°κ³νμ΅ κΈ°λ°μ μμΈ‘ λͺ¨λΈλ§μΌλ‘ μ κ·ΌνκΈ° μ΄λ €μ΄ n -\> p λλ n \< p μΈ μλ£μ μμΈ‘ λͺ¨λΈλ§μ κ΄ν λ°©λ²λ‘ (μ¬κΈ°μ nμ κ΄μΈ‘μΉμ μ, pλ μμΈ‘λ³μμ μ)
- κΌ κ³ μ°¨μ μλ£κ° μλ, νκ·λͺ¨νμ μμΈ‘ μ±λ₯μ λμ΄κΈ° μν΄μλ μ¬μ©λλ λ°©λ²λ‘ λ€μ ν΄λΉ
- ν΅κ³μ κ°μ€κ²μ κ΄μ μμ κ°μ€ κ²μ μ λ°μνλ κ³ μ°¨μ λ¬Έμ μ κ΄ν μ루μ
λν μ 곡ν¨
### 1 κ³ μ°¨μ μλ£μ κ΄ν μμΈ‘ λͺ¨λΈλ§
#### Prerequisites
- π [μ€ν°λ λ
ΈνΈ: Prerequisite κ³ μ°¨μ μλ£μ λν κ³ μ μ μΈ νκ·λΆμμ λ¬Έμ μ ](https://be-favorite.tistory.com/28?category=908019)
#### Ridge regression
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/29?category=908019)
#### Lasso regression
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/46?category=908019)
#### Bias reduction of Lasso estimator
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/48?category=908019)
#### Variance reduction of Lasso eistimator
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/49?category=908019)
#### Penalized logistic regression
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/50?category=908019)
#### Penalized robust regression
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/51?category=908019)
### 2 ν΅κ³μ κ°μ€κ²μ κ΄μ μ κ³ μ°¨μ λ¬Έμ
#### Prerequisites
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 1 ν΅κ³μ κ°μ€κ²μ μ μ리](https://be-favorite.tistory.com/21?category=894492)
- π [μ€ν°λ λ
ΈνΈ: Prerequisite 2 λ€μ€ κ²μ ](https://be-favorite.tistory.com/20)
#### Family-Wise Error Rates (FWER)
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/25?category=908019)
#### False Discovery Rates (FDR)
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/26?category=908019)
## Statistics
- ν΅κ³ν, ν΅κ³μ κ°μ€κ²μ κ³Ό κ΄λ ¨ν κ²λ€μ μμΉ΄μ΄λΈ ν©λλ€.
### ꡬκ°μΆμ μ ν΄μμ λν κ³ μ μ κ΄μ (Frequentist)κ³Ό λ² μ΄μ§μ κ΄μ
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/27?category=894492)
### κ²μ λ ₯(power)κ³Ό κ²μ λ ₯ ν¨μμ λν΄
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/22?category=894489)
### μμ λ(Degrees of Freedom)
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/44?category=894492)
### νμ€νΈμ°¨μ νμ€μ€μ°¨
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/45?category=894492)
### "λ립κ°μ€μ΄ μ³λ€."λΌλ μμ μ£Όμ₯μ μ§μν΄μΌνλ μ΄μ
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/66?category=894492)
### μ€μ¬κ·Ήνμ 리μ μλ―Έ
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/70?category=894492)
- π [μ€ν°λ λ
ΈνΈ: μ€μ¬κ·Ήνμ 리μ κ΄ν κ³ μ°°](https://statisticsplaybook.tistory.com/67?category=924794)
### Fixed effectμ random effect
- π [μ€ν°λ λ
ΈνΈ](https://be-favorite.tistory.com/19?category=904635)
## Miscellaneous
### κ²°μ λ‘ μ SIR λͺ¨νμ μ΄μ©ν κ°μΌλ³ μ ν λͺ¨λΈλ§
- π [μ€ν°λ λ
ΈνΈμ R νν 리μΌ](https://github.com/be-favorite/Tutorials_SIR-models)