Using the R packages tidyquant and BatchGetSymbols.
Cumulative distribution function, quantile function, hazard function, and cumulative hazard function of the Generalised Weibull distribution.
Probability density function, cumulative distribution function, quantile function, random number generation, hazard function, and cumulative hazard function of the Exponentiated Weibull distribution.
Modelling bivariate log returns using a Gaussian copula with twopiece and double twopiece marginals
Algorithms and code for simulating from joint models for longitudinal and survival data with time dependent effects.
The BTV(1,1) prior for the Power Generalised Weibull Distribution
The Generalised Gamma Distribution and a link with the Gamma Distribution
Simulation of normal corrrelated covariates: implementation and a warning about the correlation matrix
Parametric Survival Models
Parametric Survival Regression Models
Quick and dirty parallelisation of a for loop
Implementation of the pdf, cdf, quantile function, and random number generation of the DTP SAS distribution using the DTP R package.
Likelihood Ratio Test: Skew Normal vs Normal
Bayesian inference on the two-piece normal distribution using rstan. Simulated and Real Data applications.
Implementation of the softmax function using the recursive formula.
pdf, the cdf, the quantile function, random number generation, and moments associated to the Hyperbolic Secant distribution.
This R markdown contains illustrative examples about the family of two-piece distributions and the "twopiece" R package.
The LogSumExp function is the logarithm of the sum of the exponentials of n values. This R Markdown presents 5 methods to calculate the LogSumExp function.
The Power Generalised Weibull Distribution in R: a three-parameter distribution with positive support and flexible hazard function.
The Cox Proportional Hazards Model and the Partial Likelihood function
R code and illustrations of the behaviour of the Laplace Inverse Mills Ratio (LIMR) and its first derivative.
This tutorial shows how to simulate from a General Hazard structure that includes time dependent effects as well as effects that only affect the hazard level.
R code to simulate from a bivariate distribution based on a Gaussian copula
pdf, the cdf, the quantile function, random number generation, and moments associated to the Kumaraswamy distribution.
A simulated data example
MLE for the location and scale parameters in the Logistic Distribution
Life spans of wild type vs. transgenic mosquitoes
t-intervals: paired observations
Estimating a proportion $\theta$ and its relationship with the number of Monte Carlo simulations
Some examples of permutation tests using the difference of means and the Kolmogorov-Smirnov test statistic
Robust Outlier Detection vs. Non-Robust Outlier Detection
Mean vs. Median
Normalised Median Absolute Deviation vs. Standard Deviation
A Bayesian Analysis of a Binomial Trial.
Second order approximation: Wald confidence intervals for the Normal distribution
Analysis of the Challenger disaster data using logistic regression
Profile likelihood confidence intervals for the parameters of the normal distribution (mean and standard deviation)
A simulation study to check the performance of asymptotic normal CIs for the log-odds
An example where the Method of Moments does not lead to a closed form solution and requires the use of numerical methods to obtain a solution to the corresponding estimating equations.
Maximum likelihood estimation in the logistic regression model
Three examples of the numerical calculation of the Wasserstein-1 metric, including its use for comparing survival curves.
This R code illustrates the use of General Hazard structure models in a simulated data set. The data set was simulated using the General Hazards (GH) structure. The idea is to fit the parametric regression models with hazard structures PH, AH, AFT, and GH and select the one favoured by the Akaike Information Criterion (AIC).
This R code illustrates the use of General Hazard structure models in a simulated data set. The data set was simulated using the Proportional Hazards (PH) structure. The idea is to fit the parametric regression models with hazard structures PH, AH, AFT, and GH and select the one favoured by the Akaike Information Criterion (AIC).
Frequentist vs Noninformative Bayesian inference in the Binomial model using Uniform and Jeffreys priors.
Some visual tools for comparing two univariate samples
A real data example of linear mixed models for censored responses with flexible random effects and flexible residual errors.
A real data application of linear mixed models with flexible errors and flexible random effects.
Two different methods to create a Secret Santa random list in R from a list of names.
Illustration of some properties of MCMC samplers
The predictive distribution for a Binomial sampling model with Beta prior.
The posterior distribution of the mean for a normal sampling model with known variance and normal prior distribution
A short description of the Binomial distribution, the Beta distribution, and the Bayesian Beta-Binomial model.
Some properties of the Inverse Mills Ratio
R codes to implement kernel density and distribution estimators for data with support on R, R_+, and (0,1) by using a transformation approach.
Tractable Bayesian Variable Selection: Beyond normality. Analysis of DLD data using two-piece residual errors and non-local priors.
An objective prior for the number of degrees of freedom of a multivariate t distribution
The Jeffreys prior for the skewness parameter in skew–symmetric models
A tractable, scalable, expression for the Kullback Leibler divergence between a multivariate t and a multivariate normal distributions
A tractable, scalable, expression for the Kullback Leibler divergence between two multivariate t distributions
An application of an objective prior for the number of degrees of freedom of a multivariate t distribution
A financial application of an objective prior for the number of degrees of freedom of a multivariate t distribution
Several types of Nonparametric estimators of P(X<Y) for paired data
Galton’s Forecasting Competition data modelling using the DTP R package.
Implementation of a weakly informative prior for the degrees of freedom of the t distribution.
Mollification of two-piece distributions.
Implementation of the Laplace and two piece Laplace distributions (using the R package twopiece).
Real data example to illustrate the use of Jeffreys and Total Variation priors for the shape parameter of the skew-normal distribution
A real data example to illustrate how to fit a t-copula with t and two piece t marginals
The TPSAS R package implements the univariate two-piece sinh–arcsinh distribution
Implementation of the probability density function, cumulative distribution function, quantile function, and random number generation of the SAS distribution.
Bayesian inference for the ratio of the means of two normal populations with unequal variances using reference priors.
A simple approach to maximum intractable likelihood estimation: AMLE. Two toy examples.
Implementaion of the distribution of the ratio of two independent normal distributions and a normal approximation.
Implementation of Posterior QQ envelopes for normality test.
Implementation of Posterior QQ envelopes and predictive QQ plots in the context of linear regression.
Bayesian AFT models with two-piece errors
Bayesian Accelerated Failure Time models with skew-symmetric errors.
Accelerated failure time models with two-piece errors using maximum likelihood estimation.
The twopiece R package implements the family of Two-Piece distributions.
The DTP R package implements the family of Double Two-Piece distributions.
Description and implementation of the two-piece Generalised Hyperbolic distribution.
Implementation and description of the two-piece Variance Gamma distribution.
Implementation and description of the two-piece Johnson-SU distribution