1 Các chủ đề thường gặp

1.1 Person

1.1.1 Statistician

Samiran Sinha

https://samiransinha.github.io/teaching/

Laurent Smeets

https://www.rensvandeschoot.com/colleagues/laurent-smeets/

1.1.2 Psycholinguist

Luca Campanelli

https://www.lcampanelli.org/

1.2 Dataset

Vanderbilt Biostatistics

https://hbiostat.org/data/

Datasets for the survival data modelling on engineering applications

https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data#overviewHardDriveData

Clinical proteomic datasets from NCI

http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp

Kaggle, a platform for different kinds of data used for data science competitions.

https://www.kaggle.com/data

It is a repository of shared datasets available through AWS resources. https://registry.opendata.aws/`

1.3 Mixed effects model

Mixed effects model analysis using R

http://samiransinha.github.io/files/teaching/685part1.html

Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. “Fitting Linear Mixed-Effects Models Using Lme4.” Journal of Statistical Software 67, no. 1 (2015).

https://doi.org/10.18637/jss.v067.i01

http://book.thuviencanhan.com:8033/results?query=%22Bates+et+al.+-+2015+-+Fitting+Linear+Mixed-Effects+Models+Using+lme4.pdf%22&dir=%3Call%3E&after=&before=&sort=relevancyrating&ascending=0&page=1

Bates, Douglas M. Lme4: Mixed-Eﬀects Modeling with R. 2022.

https://people.math.ethz.ch/~maechler/MEMo-pages/lMMwR.pdf

Luca Campanelli. Introduction to mixed-effects modeling using the lme4 package.

https://web.archive.org/web/20230313184038/https://www.lcampanelli.org/mixed-effects-modeling-lme4/

LME4 Tutorial: Popularity Data

https://www.rensvandeschoot.com/tutorials/lme4/

Fixed vs Random vs Mixed Effects Models – Examples

https://vitalflux.com/fixed-vs-random-vs-mixed-effects-models-examples/

What is a difference between random effects-, fixed effects- and marginal model?

https://stats.stackexchange.com/questions/21760/what-is-a-difference-between-random-effects-fixed-effects-and-marginal-model

Concepts behind fixed/random effects models

https://stats.stackexchange.com/questions/33984/concepts-behind-fixed-random-effects-models

A brief introduction to mixed effects modelling and multi-model inference in ecology

https://pmc.ncbi.nlm.nih.gov/articles/PMC5970551/

1.4 Survival analysis

Hosmer, David W., Stanley Lemeshow, and Susanne May. Applied Survival Analysis: Regression Modeling of Time‐to‐Event Data. John Wiley & Sons, Ltd, 2008.

https://doi.org/10.1002/9780470258019.fmatter

http://book.thuviencanhan.com:8033/results?query=%22Hosmer+et+al.+-+2008+-+Applied+Survival+Analysis+Regression+Modeling+of+Time%E2%80%90to%E2%80%90Event+Data.pdf%22&dir=%3Call%3E&after=&before=&sort=relevancyrating&ascending=0&page=1

1.5 B-splines

A short note on B-splines, and two related files for computing spline basis functions R script, Fortran subroutines

http://samiransinha.github.io/files/teaching/note1.pdf

http://samiransinha.github.io/files/teaching/code4Splines.R

http://samiransinha.github.io/files/teaching/spline.f

https://samiransinha.github.io/teaching/

1.6 Epidemiology

1.6.1 Case-control study

Case-control studies in epidemiological research

http://samiransinha.github.io/files/presentation/TAMU_Vet_School_Nov2021.pdf

1.7 Single cell RNAseq

Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs

https://samiransinha.github.io/files/presentation/WNAR2023_presentation.pdf

1.8 Multilevel analysis

Multilevel analysis: Techniques and applications

https://multilevel-analysis.sites.uu.nl/

1.9 Bayesian

Bürkner, (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28.

https://doi.org/10.18637/jss.v080.i01

Magnusson et al. (2019). Bayesian leave-one-out cross-validation for large data (2019)

https://proceedings.mlr.press/v97/magnusson19a/magnusson19a.pdf

Vehtari et al (2013). Understanding predictive information criteria for Bayesian models.

https://sites.stat.columbia.edu/gelman/research/published/waic_understand3.pdf

Vehtari et al. (2018). R-squared for Bayesian regression models

http://www.stat.columbia.edu/~gelman/research/unpublished/bayes_R2.pdf

Vehtari et al. (2019). Bayesian R2 and LOO-R2

https://avehtari.github.io/bayes_R2/bayes_R2.html

Vehtari et al. (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC (with discussion). Bayesian Data Analysis.

https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Rank-Normalization-Folding-and-Localization--An-Improved-R%CB%86-for/10.1214/20-BA1221.full

1.10 Randomness

https://en.wikipedia.org/wiki/Randomness#cite_note-5

1.11 Normal distribution

https://en.wikipedia.org/wiki/Normal_distribution

\[f(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2 \sigma^2}}\]

1.12 Sample size

How to calculate sample size in randomized controlled trial?

https://pubmed.ncbi.nlm.nih.gov/22263004/

2 Sách tham khảo

(Crainiceanu, Caffo, and Muschelli 2018)

--- aliases: - "/introduction_statistics.html" editor_options: chunk_output_type: console bibliography: references.bib --- # Các chủ đề thường gặp ## Person ### Statistician Samiran Sinha `https://samiransinha.github.io/teaching/` Laurent Smeets `https://www.rensvandeschoot.com/colleagues/laurent-smeets/` ### Psycholinguist Luca Campanelli `https://www.lcampanelli.org/` ## Dataset Vanderbilt Biostatistics `https://hbiostat.org/data/` Datasets for the survival data modelling on engineering applications `https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data#overviewHardDriveData` Clinical proteomic datasets from NCI `http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp` Kaggle, a platform for different kinds of data used for data science competitions. `https://www.kaggle.com/data` It is a repository of shared datasets available through AWS resources. https://registry.opendata.aws/\` ## Mixed effects model Mixed effects model analysis using R `http://samiransinha.github.io/files/teaching/685part1.html` Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. “Fitting Linear Mixed-Effects Models Using Lme4.” Journal of Statistical Software 67, no. 1 (2015). `https://doi.org/10.18637/jss.v067.i01` `http://book.thuviencanhan.com:8033/results?query=%22Bates+et+al.+-+2015+-+Fitting+Linear+Mixed-Effects+Models+Using+lme4.pdf%22&dir=%3Call%3E&after=&before=&sort=relevancyrating&ascending=0&page=1` [Bates, Douglas M. Lme4: Mixed-Eﬀects Modeling with R. 2022.]{style="color:red;"} `https://people.math.ethz.ch/~maechler/MEMo-pages/lMMwR.pdf` Luca Campanelli. Introduction to mixed-effects modeling using the `lme4` package. `https://web.archive.org/web/20230313184038/https://www.lcampanelli.org/mixed-effects-modeling-lme4/` LME4 Tutorial: Popularity Data `https://www.rensvandeschoot.com/tutorials/lme4/` Fixed vs Random vs Mixed Effects Models – Examples `https://vitalflux.com/fixed-vs-random-vs-mixed-effects-models-examples/` What is a difference between random effects-, fixed effects- and marginal model? `https://stats.stackexchange.com/questions/21760/what-is-a-difference-between-random-effects-fixed-effects-and-marginal-model` Concepts behind fixed/random effects models `https://stats.stackexchange.com/questions/33984/concepts-behind-fixed-random-effects-models` A brief introduction to mixed effects modelling and multi-model inference in ecology `https://pmc.ncbi.nlm.nih.gov/articles/PMC5970551/` ## Survival analysis Hosmer, David W., Stanley Lemeshow, and Susanne May. Applied Survival Analysis: Regression Modeling of Time‐to‐Event Data. John Wiley & Sons, Ltd, 2008. `https://doi.org/10.1002/9780470258019.fmatter` `http://book.thuviencanhan.com:8033/results?query=%22Hosmer+et+al.+-+2008+-+Applied+Survival+Analysis+Regression+Modeling+of+Time%E2%80%90to%E2%80%90Event+Data.pdf%22&dir=%3Call%3E&after=&before=&sort=relevancyrating&ascending=0&page=1` ## B-splines A short note on B-splines, and two related files for computing spline basis functions R script, Fortran subroutines `http://samiransinha.github.io/files/teaching/note1.pdf` `http://samiransinha.github.io/files/teaching/code4Splines.R` `http://samiransinha.github.io/files/teaching/spline.f` `https://samiransinha.github.io/teaching/` ## Epidemiology ### Case-control study Case-control studies in epidemiological research `http://samiransinha.github.io/files/presentation/TAMU_Vet_School_Nov2021.pdf` ## Single cell RNAseq Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs `https://samiransinha.github.io/files/presentation/WNAR2023_presentation.pdf` ## Multilevel analysis Multilevel analysis: Techniques and applications `https://multilevel-analysis.sites.uu.nl/` ## Bayesian Bürkner, (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28. `https://doi.org/10.18637/jss.v080.i01` Magnusson et al. (2019). Bayesian leave-one-out cross-validation for large data (2019) `https://proceedings.mlr.press/v97/magnusson19a/magnusson19a.pdf` Vehtari et al (2013). Understanding predictive information criteria for Bayesian models. `https://sites.stat.columbia.edu/gelman/research/published/waic_understand3.pdf` Vehtari et al. (2018). R-squared for Bayesian regression models `http://www.stat.columbia.edu/~gelman/research/unpublished/bayes_R2.pdf` Vehtari et al. (2019). Bayesian R2 and LOO-R2 `https://avehtari.github.io/bayes_R2/bayes_R2.html` Vehtari et al. (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC (with discussion). Bayesian Data Analysis. `https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Rank-Normalization-Folding-and-Localization--An-Improved-R%CB%86-for/10.1214/20-BA1221.full` ## Randomness `https://en.wikipedia.org/wiki/Randomness#cite_note-5` ## Normal distribution `https://en.wikipedia.org/wiki/Normal_distribution` $$f(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2 \sigma^2}}$$ ## Sample size How to calculate sample size in randomized controlled trial? `https://pubmed.ncbi.nlm.nih.gov/22263004/` # Sách tham khảo [@crainiceanu2018]