TStat - TStat Training

646 posts

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TStat - TStat Training

TStat - TStat Training

@TStatSrl

TStat offers biostatistic, econometric and statistical training and statistical consultancy to researchers and professionals in the private and public sector.

Italia Katılım Ağustos 2014
225 Takip Edilen392 Takipçiler
TStat - TStat Training
TStat - TStat Training@TStatSrl·
to implement hierarchical clustering using TF-IDF vectorization & cosine distance. Allowing users to identify patterns on data, uncover latent themes & organize information for macroeconomic forecasting, sentiment analysis, or real-time policy monitoring. bit.ly/42bAyxD
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Combining native Stata capabilities with Python-enhanced workflows, Carlo Drago offers an accessible and policy-relevant toolkit for unsupervised text classification in multiple domains. This applied text mining & clustering framework enables users to classify & interpret text
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Loreta Isaraj illustrating automated data extraction from unstructured textual sources using a scalable fully integrated workflow employing Large Language Models (LLMs), specifically @ChatGPT 4.0 via @API, in conjunction with @Python & @Stata. Details @ bit.ly/481VM4C
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Covariate selection is performed through a Lasso-based local estimation, ensuring valid inference under approximate sparsity. The command generates output variables that can be used for further post-estimation analysis within the same session. More @ bit.ly/3KdQY2n
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Marianna Nitti currently illustrating her NEW @stata command rdlasso, which implements Regression Discontinuity Designs with high-dimensional covariates in @Stata. The procedure extends the methodology developed by Kreiss and Rothe (2023) to both sharp and fuzzy designs.
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
bootstrap sampling, and optimized #Mata routines to compute the co-association matrix, ensuring computational efficiency.. Download Carlo’s presentation @ bit.ly/4gJO8OE
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
co-association matrix, which allows @stata users to address the possible inherent instability of partitioning-based #clustering by aggregating results from multiple #bootstrap samples, improving robustness and reproducibility. Carlo uses a combination of existing Stata routines,
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Are you trying to group data into sets of similar observations without relying on predefined labels? Then Carlo Drago’s work on consensus clustering in @Stata 19 is likely to be of interest. Carlo’s approach combines bootstrapped k-means with hierarchical clustering based on a
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Maria Elena Bontempo illustrating how her NEW flexible panel data command pantarei extends the existing commands xtsum & loneway to consider, amongst other things, the role of CCE inside the within variability. Presentation at bit.ly/4gGHgBt
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Jan Ditzen's panel data xttools suite of commands opening our Tips & Tricks session this morning: xtpcaget obtains principal components from multiple variables; xtplot2 identifies panel structure for variables
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
& xtcorri calculates unit specific correlations or covariances. More details @ bit.ly/4nwFFRi - Commands available on request from Jan!
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Jeff Pitblado from @StataCorp, kicking off the Italian @Stata User Conference this morning here in #Milan, showing us the best way to exploit @stata's frame command features, such as frlink - used to link data stored in different frames! Presentation at bit.ly/46lGAy5
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TStat - TStat Training
TStat - TStat Training@TStatSrl·
Join us and Robert Grant on our Intro to #Bayesian #MetaAnalysis in @Stata on 26/9/25 to learn how to re-conceptualise meta-analysis from a weighted average estimator to a probabilistic model, to estimate common, random effect & Network MA. Full details @ bit.ly/3HzGzNq
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