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A personal blog about data visualization and data analysis.
Welcome to my blog!
Here I will share some ideas about dataviz and data science.
- Periodic splines;/statistics/periodic_splines;1
- Directional statistics;/statistics/directional;2
- Prior sensitivity analysis;/statistics/sensitivity;3
- Horseshoe priors;/statistics/horseshoe;4
- MRP;/statistics/mrp;5
- Application of the Lotka-Volterra model;/statistics/lotka_volterra;6
- Differential equations;/statistics/ode;7
- Dirichlet Process Mixture Models;/statistics/dp;8
- Bayesian Additive Regression Trees;/statistics/bart;9
- Splines;/statistics/spline;10
- Gaussian processes regression;/statistics/gp_example;11
- Gaussian processes;/statistics/gp;12
- Nonparametric models;/statistics/nonparametric_intro;13
- Stochastic volatility models;/statistics/stochastic_volatility;14
- Structural time series;/statistics/structural_time_series;15
- Time series;/statistics/time_series;16
- Quantile regression;/statistics/extreme_quantile;17
- Introduction to Extreme Values theory;/statistics/extreme_intro;18
- Frailty models and cure rate models;/statistics/survival_example_frailty;19
- Accelerated Failure Time models;/statistics/survival_example_aft;20
- Application of survival analysis with discrete times;/statistics/survival_example_2;21
- Application of survival analysis 1;/statistics/survival_example;22
- Introduction to survival analysis;/statistics/survival_analysis;23
- Regression discontinuity design;/statistics/rdd;24
- Difference in difference;/statistics/difference_in_differences;25
- Instrumental variable regression;/statistics/instrumental_variable;26
- Randomized controlled trials;/statistics/randomized;27
- Validity;/statistics/validity;28
- Causal inference and Bayesian networks;/statistics/causal_intro_2;29
- Causal inference;/statistics/causal_intro;30
- Nested factor;/statistics/nested_factors;31
- Repeated measures;/statistics/repeated_measures;32
- Split plot design;/statistics/split_plot;33
- Crossover design;/statistics/crossover;34
- Latin square design;/statistics/latin_square;35
- Full factorial design;/statistics/full_factorial;36
- Completely randomized design;/statistics/crd;37
- Design of experiments;/statistics/doe;38
- Stratification;/statistics/stratification;39
- Random sampling;/statistics/random_sampling;40
- Data collection;/statistics/data_collection;41
- Things that could go wrong;/statistics/problem_solving_issues;42
- The problem solving workflow;/statistics/problem_solving;43
- Mixed effects models with more than two levels;/statistics/three_levels;44
- Leveraging mixed-effect models;/statistics/bambi_multilevel;45
- Random models and mixed models;/statistics/random_models;46
- Hierarchical models and meta-analysis;/statistics/hierarchical_metaanalysis;47
- Hierarchical models;/statistics/hierarchical_models;48
- Poisson regression;/statistics/poisson_regression;49
- Logistic regression;/statistics/logistic_regression;50
- Robust linear regression;/statistics/robust_regression;51
- Multi-linear regression;/statistics/multivariate_regression;52
- Linear regression with binary input;/statistics/regression_binary_input;53
- Introduction to the linear regression;/statistics/regression;54
- Model comparison, cont.;/statistics/model_averaging_cont;55
- Model comparison;/statistics/model_averaging;56
- Re-parametrizing your model;/statistics/reparametrization;57
- Predictive checks;/statistics/predictive_checks;58
- Trace inspection;/statistics/trace_inspection;59
- Introduction to the Bayesian workflow;/statistics/bayesian_workflow;60
- Mixture models;/statistics/mixture;61
- Multidimensional distributions;/statistics/categories;62
- The Gaussian model;/statistics/reals;63
- Bonus: counting animals in a park;/statistics/hypergeom;64
- The Negative Binomial model;/statistics/negbin;65
- The Poisson model;/statistics/poisson;66
- The Beta-Binomial model;/statistics/betabin;67
- Section introduction;/statistics/simple_models_intro;68
- Some notation about probability;/statistics/probability_reminder;69
- How does MCMC works;/statistics/mcmc_intro;70
- Introduction to Bayesian inference;/statistics/bayes_intro;71
- An overview to statistics;/statistics/preface;72
- The Gestalt principles;/dataviz/gestalt;73
- Design tricks;/dataviz/design-introduction;74
- How to choose a color map;/dataviz/palettes-introduction;75
- Introduction to color perception;/dataviz/color-introduction;76
- Drawing is redrawing;/dataviz/gender-economist;77
- Visual queries;/dataviz/visual-queries;78
- Channel effectiveness;/dataviz/effectiveness;79
- Evolutions of the line chart;/dataviz/linechart-evolution;80
- Beyond the 1D scatterplot;/dataviz/scatterplot-evolution;81
- Perception;/dataviz/perception;82
- Fundamental charts;/dataviz/fundamental-charts;83
- Marks and channels;/dataviz/marks-channels;84
- Data abstraction;/dataviz/data-types;85
- Data visualization;/dataviz/dataviz;86
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