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Alexis Agustín

See how Deloitte uses R for actuarial analysis - 0 views

blog.revolutionanalytics.com/...-r-for-actuarial-analysis.html

Deloitte Analisis de datos Regression Poisson extrapolacion

shared by Alexis Agustín on 25 Apr 13 - No Cached
    • Alexis Agustín
      Alexis Agustín on 25 Apr 13
       
      Aqui un consultor de Deloitte nos muestra como usa R para explorar datos y ajustarlos en distribuciones, calculando proyecciones usando la regresion de Poisson. Ademas lo contrapone a Excel, concluyendo que no hay una hoja de calculo en la que confundir columnas, si no que en lineas de codigo simple que se puede copiar se puede trabajar mas facilmente con grandes cantidades de datos
  • Jim Guszcza (Predictive Analytics lead at Deloitte Consulting and Assistant Professor at UW-Madison) who gave a great webinar presentation yesterday
  • R is used for exploratory data analysis and modeling, with a live examples of fitting a mixute distribution to bimodal claims data, and calculating loss reserves using Poisson regression.
  • ...2 more annotations...
  • Just one simple line of [R] code that would work just as well for a 100-by-100 loss triangle as it would for a 10-by-10 triangle. No hidden cells in the spreadsheet, no risk of spreadsheet error. It's a little bit of code you could look at in one screen, it's replicable ... and this does all the work that a spreadsheet would do. 
  • He uses the Allstate Claim Prediction Challenge data (from a recent Kaggle competition) to fit a Tweedie model to 13 million records of claim data. (The Tweedie distribution is often used to model insurance claims, where many claims are exactly zero, and non-zero claims follow a continuous Gamma-like distribution.) Using the forthcoming rxGLM function, he fit the model to this large data set in just over two minutes (140.22 seconds) using a single quad-core PC.
Alpha Teleco

R Tutorial Series: Basic Polynomial Regression | (R news & tutorials) - 0 views

www.r-bloggers.com/es-basic-polynomial-regression

tutorial series basic basico regression regresion news tutorials polynomial no lineal

shared by Alpha Teleco on 29 Apr 13 - No Cached
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Tutorial regresión no lineal basica en R.
  • Polynomial regression can be used to explore a predictor at different levels of curvilinearity.
  • This tutorial will demonstrate how polynomial regression can be used in a hierarchical fashion to best represent a dataset in R.
  • ...24 more annotations...
  • Scatterplot
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      El grafico de dispersion demuestra que estos datos pueden no ser lineales.
  • The preceding scatterplot demonstrates that these data may not be linear.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Como crear variables de orden lineal, cuadratico y cubico.
  • Creating The Higher Order Variables
  • In this tutorial, we will explore the a linear, quadratic, and cubic model.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Paso 1: centrar la variable, restando la media de cada punto y guardando el resultado en una nueva variable de R.
  • Step 1: Centering
  • To center a variable, simply subtract its mean from each data point and save the result into a new R variable, as demonstrated below.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Paso 2: Crear los terminos de orden superior multiplicando la variable creada por si misma y guardando el resultado en una nueva variable.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Crear los modelos lineales y no lineales.
  • Once the input variable has been centered, the higher order terms can be created.
  • we can simply multiply our centered term from step one and save the result into a new R variable, as demonstrated below.
  • Step 2: Multiplication
  • Creating The Models
  • Now we have all of the pieces necessary to assemble our linear and curvilinear models.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Evaluar los modelos y compararlos, en el ejemplo se observa que no ha una diferencia significativa entre los tres modelos.
  • Evaluating The Models
  • In this case, the quadratic and cubic terms are not statistically significant themselves nor are their models statistically significant beyond the linear model.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      A traves de los modelos que hay en este tutorial se pueden crear modelos muy complejos, utilizando las mismas sencillas tecnicas de este tutorial.
  • it can be helpful to summarize and compare our potential models using the summary(MODEL) and anova(MODEL1, MODEL2,… MODELi) functions.
  • More On Interactions, Polynomials, and HLR
  • The regression topics covered in these tutorials can be mixed and matched to create exceedingly complex models.
  • The good news is that more complex models can be created using the same techniques covered here. The basic principles remain the same.
  • Alpha Teleco on 29 Apr 13
     
    Tutorial
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