Modern Methods For Robust Regression Pdf To Excel
Posted : admin On 11.01.2020Should you use linear or logistic regression? In what contexts? There are hundreds of types of regressions.
Modern methods for robust regression pdf reader. Here is an overview for data scientists and other analytic practitioners, to help you decide on what regression to use depending on your context. Many of the referenced articles are much better written (fully edited) in my.to see source, for this picture. Linear regression: Oldest type of regression, designed 250 years ago; computations (on small data) could easily be carried out by a human being, by design. Can be used for interpolation, but not suitable for predictive analytics;, e.g. Sensitivity to both ouliers and cross-correlations (both in the variable and observation domains), and subject to over-fitting. A better solution is piecewise-linear regression, in particular for time series.
Logistic regression: Used extensively in clinical trials, scoring and fraud detection, when the response is binary (chance of succeeding or failing, e.g. For a new tested drug or a credit card transaction). Suffers same drawbacks as linear regression (not robust, model-dependent), and computing regression coeffients involves using complex iterative, numerically unstable algorithm. Can be well approximated by linear regression after transforming the response (logit transform). I'm puzzled why there isn't more attention here to the underlying model. If you have strong reason to believe that the underlying model is linear, then linear regression is fine. If you have strong reason to believe it's sigmoidal, then linear regression is an unlikely candidate.
What it usually boils down to, in my experience, is defining the model, and defining the norm. Answers to those two questions pretty much define the problem that you are solving, and given that, there is a (usually) unique solution. It is frustrating to me when I see people typing stuff in at the keyboard but they don't have a solid description of the problem they are solving. Once you have that problem definition, the specific method of solution is often pretty clear. Comment by on April 7, 2015 at 9:48am.
Another type of regression that I find very useful is Support Vector Regression, proposed by Vapnik, coming in two flavors:SVR - (python - sklearn.svm.SVR) - regression depends only on support vectors from the training data. The cost function for building the model ignores any training data epsilon-close to the model prediction.NuSVR - (python - sklearn.svm.NuSVR), enabling to limit the number of support vectors used by the SVR.As in support vector classification, in SVR different kernels can be used in order to build more complex models using the kernel trick.Comment by on July 31, 2014 at 7:42pm.
About R implementations, here is a comment by Alan Parker (see also Amy's comment below):The CRAN task view: “Robust statistical methods” gives a long list of regression methods, including many that Vincent mentions. Here a some that are not mentioned there:Regression in unusual spaces. This subject is old. It is usually addressed under the title “Compositional data” (see Wikipedia entry). The late John Aitchison founded this area of statistics. Googling his name + “compositional data” gives access to a number of his articles. The R package “compositions” deals with it comprehensively.
Another package treats the problem using robust statistics: “robCompositions”.Bayesian regression. I find Bayesian stuff conceptually hard, so I am using John Kruschke’s friendly book: “Doing Bayesian data analysis”. Chapter 16 is on linear regression. He provides a free R package to carry out all the analyses in the book. The CRAN view “Bayesian” has many other suggestions. Package BMA does linear regression, but packages for Bayesian versions of many other types of regression are also mentioned.
Comment by on July 24, 2014 at 5:00am. Hi VincentI was thinking about the class of regressions where the data vary over time, say time series.
Modern Methods For Robust Regression Pdf To Excel Free
You may know that Econometric Methods contain a lot of alternative versions of regressions depending upon the type of violation of basic assumptions of linear model. You are right when you say jackknife and transformations may take some of these issues but not all. Thus there are regressions with appropriate transformations to control heteroscedasticity; regressions with AR(1) disturbances; regressions with distributed lags or geometric lag structure of explanatory variables; regressions with lagged explained variables leading to partial adjustment and adaptive expectation model; regressions with stochastic regressors; regressions with error in measurement leading to regression with instrumental variables. Above all the problem of co-integrated models in regression. I was just adding to your list.Kalyanaraman. ‹ Previous. 1.
Robust Regression Pdf
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