Model selection is a problem of judging which model from some set performs best. Popular methods include $R^2$, AIC and BIC criteria, test sets, and cross-validation. To some extent, feature selection is a subproblem of model selection.

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How to account for different ratio of samples during training and detection using a support vector machine (svm)?

Consider the following object recognition case: Detection of objects in an image using a sliding window approach in combination with a svm model. During sliding window search using multiple scale ...
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How to find a good model for an object recognition case using a support vector machine (svm)?

Consider the following example of an object recognition case: I'm trying to detect objects in an image using histograms of oriented gradients (hog) features. The feature vector resulting from hog is ...
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Using K-fold cross validation to select a model's parameters

I think I understand completely the concept of cross validation, but there is one aspect I've never seen detailed. Let's assume I have a logistic regression model with four parameters I want to train. ...
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Relative variable importance for simple model set

I am evaluating models based on AIC. I started by running the simplest models and the dot model (no covariates) is the best model, with little support for any others. When reporting the relative ...
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42 views

Correct numerical result in Bayesian model comparison

I was wondering how to calculate the following Bayesian model comparison. Suppose you have a couple of models: $$M_{1}: x \sim Bin(n, \pi); p \sim Be(1,1)$$ $$M_{2}: x \sim Bin(n, \pi); p \sim ...
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multimodel inference when using rms package

I would be glad to have some advise about how to proceed with multimodel inference to obtain weighted estimates based on AICc after running ordinal logistic analyzes with "rms' package. I used the ...
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27 views

How to determine which covariates to use

This may seem like a basic question but here goes... I am looking at the effect of brain stimulation on skill acquisition across several timepoints. I have several measures that may be useful as ...
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Cross validation with unequal sample size for the left out sets

I am trying to do cross validation on several (20) subsets of samples, which all have unequal sample size. I cannot subsample so that sizes are equal. Example: batch 1: 500 samples batch 2: 400 ...
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How to approach hurdle models with multiple covariates in R

I have count data that I standardized into continuous data (density) because the area surveyed varied among sites. I have several sites in which the count was zero. The probability of observing a ...
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415 views

Model building and selection using Hosmer et al. 2013. Applied Logistic Regression in R

This is my first post on StackExchange, but I have been using it as a resource for quite a while, I will do my best to use the appropriate format and make the appropriate edits. Also, this is a ...
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24 views

Goodness of fit: observed vs simulated data

I have a set of 2-dimensional "observed" data of sample size N: $$O = \{(x_1, y_1), (x_2, y_2), ..., (x_N, y_N)\}$$ The hypothesis is that $O$ is a realization of ...
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Select final model after many Dev-Val splits

We have a dataset that is quite heterogeneous, and we established that different Dev-Val splits affect the outcome quite bit, even after using stratified sampling. So what we did write a loop around ...
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How to prove predictive use of a biomarker?

I have a binary endpoint (cured/not cured) and a continuous biomarker measured on each patient. Every patient recieved one of two treatments. The biomarker predicts the effect of the treatment, if ...
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Is there “infinite” universal model selection ? and Structural Risk Minimization

I ask these because I come up with an idea : If I have infinite and universal model set, then there must exist model that totally fits my data and no parameter for the model so the complexity ...
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model post-event misinformation effect on eyewitness?

I'm trying to model post event misinformation. The questions i have is the factors affecting it. and a psychological way of assessing it.
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How does one adjust for data snooping when using ACF and PACF?

ACF and PACF are routinely used for approximate identification of a time series model, e.g. as described here. Say, one takes a look at the plots and guesses that it's something like ...
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analysis strategy for selecting and/or transforming correlated continuous biomarkers to predict binary endpoint

I am given a simulation task to come up with several analysis strategies and compare their relative performances. The horizon is wide open; I appreciate all recommendations of methods and references. ...
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174 views

Can predictive power be inferred from only in-sample modelling results?

I wonder if one can tell anything about predictive power of a model if model selection and estimation was done using all available data. That is, there was no data left for "out of sample" prediction ...
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Generate mixture model from data with features

I want to build a mixture model from my data, but using features of my data to calculate each component in the model. The data: For each point I have 34 associated features. Each feature is a boolean ...
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81 views

Model selection using an artificially insignificant covariate

This is continued from my other post on model selection. Let me provide more details first. 1) I have a factorial design. Factor A has 5 levels, B has 2 levels, C has 2 levels. Let us assume that ...
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Selecting optimal model when only smoothed data available?

I have a graph of some (highly nonlinear) experimental spectrum which is obtained by smoothing results of several repeated measurements obtained by different experimental methods. The graph also ...
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Follow-on to “Training with the full dataset after cross-validation” - sequential parameter estimation

Background: Here is the background for the question, both the question itself and the answer given by Dikran Marsupial. Training with the full dataset after cross-validation? It asks about after ...
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How to develop a robust procedure to select a predictive model

Imagine you have a matrix, M, of n input variables and m values per variable. There's also a ...
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Should train and test datasets have similar variance?

If variance of test dataset is lower than the one of the train dataset is it worth splitting the data? Since we know our dataset will always be limited is it fair to select models under the above ...
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Compressed Population Complexity in Minimum Description Length (MDL)

I am studying the MDL and found it is sum of model complexity and compressed population complexity. To my understanding, model complexity refers to number of bits to encode the model, which can be ...
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Posterior Predictive Checks

I understand what the posterior predictive distribution is, and I have been reading about posterior predictive checks, although it isn't clear to me what it does yet. What exactly is the posterior ...
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Empirical Bayes vs “non-informative” priors

I am familiar with the mechanics with both methods, but don't know what factors I should consider when choosing between these two approaches for adjusting a prior. I would imagine that, on a case by ...
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model comparison when alternatives are not all nested within one another

I am running a glmm with three fixed effects: opponent 1 size ("1") opponent 2 size ("2") opponent 1 size - opponent 2 size ("diff") I am unable to run all three variables in the model at once ...
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Determine which variable or variables is/are the most efficient to predict the outcome

I have a small dataset (n=74) with a +/- 50 variables, not the best data but I have to work with it. The variables are used to select a product. I want to determine which variable or variables is/are ...
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Logistic regression, Chi-square, and study design

I have a study in which I have developed a new predictor (binary) for a disease (also a binary variable). The study has two parts. In the first part, I want to test if my predictor is strongly ...
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Model selection: where to start? [closed]

For a general modeling problem, there are literally at least a dozen choices of statistical and algorithmic models to choose from. Off the top of my head, choices could be: regression (and its ...
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40 views

Comparing two models

I am interested in comparing two logistic regression models. The two models are nested: model 1 contains all predictors, and model 2 contains all predictors except 1. My goal is to test if removing ...
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29 views

selecting a link function for GLM's

If you don't care about using GLM model parameters to predict anything, but simply want to select the best-fitting model for your data, is it necessary to get into the theoretical debate as to which ...
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the role of AIC versus p-values in model selection

Let's say you are trying to choose between two models. One has two significant fixed effects. The other includes only one of the two fixed effects from the aforementioned model but has a lower AIC ...
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Model selection using mean AIC for very huge data sets

I want to select a model which best performs for a very huge data set. However, the data set is too large to calculate a model within reasonable time. If this is the case, is the following a ...
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118 views

How to choose between exponential and gamma distributions

I have same data and I would like to choose a model for it. To start with I fit an exponential distribution and a gamma distribution. Now I wanted to do a simple likelihood ratio test . However, I ...
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True discovery rate or intrinsic sensitivity?

I'm trying to evaluate my species distribution model by the rate of discovered\predicted localities. For example, my model at a stated threshold predicts 50 grid suitable cells. Then, after field ...
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GEE in SPSS: the case of ever-decreasing QICc values

I've been using GEEs in SPSS 22 to analyze my dissertation data and have discovered an interesting problem: when trying to figure out which subset of model factors have the lowest QICc, and therefore ...
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25 views

Determining if time has influence on correlation between x and y

Is there a sort of statistics model I could use to determine if time has an influence on the correlation between two sets of data? For example say I have a column $X$ and a column $Y$, both just ...
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Why is Lasso regression for high dimensional data better than Stepwise AIC?

I know Lasso eventually set some parameters to zero, acting like variable selection. I also read from paper talking about automated variable selection method like Stepwise AIC can be troublesome. So ...
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How to deal with categorical features.

Recently I am playing in the famous Big-Data website Kaggle. There is a Display Advertising Challenge. In this competition, you are giving a training file which include huge records. the records is ...
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Why is $R^2$ poor for AR model selection used for forecasting?

There is a related question here, about how to calculate the R-squared on a regression with ARIMA errors. I found the answer quite useful, and hoped for some elaboration, particularly on Rob's closing ...
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Model selection in nlme's

I can think of four ways to perform model selection nlme's: LRT, AIC, ...
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Number of parameters for AIC for a particular model

I know there have been a few well answered questions on this topic, but i have found myself in a bit of a special case this time. I am using AIC for model selection, and i am having trouble counting ...
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Understanding the Rank Probability Score

The ranked probability score (RPS) is a measure of how good forecasts that are expressed as probability distributions are in matching observed outcomes. Both the location and spread of the forecast ...
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83 views

Testing the variance part of a Generalized Linear Model out of sample

Suppose I have a response vector and a factorial design (for simplicity, assume it’s a one-way ANOVA with two treatments). A few Generalized Linear Models (Poisson, Negative Binomial, etc) are fitted ...
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What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
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Choosing models with similar AIC values [duplicate]

I'm using a multinomial logistic regression analysis to examine deer behavioural responses to camera traps in terms of 7 predictors (both singly and their interactions). I have found that the model ...
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6 views

Looking for catalog of “mechanism classes” that give rise to specific curve shapes

(I apologize for the length of this post. I don't know how to frame the question more succinctly.) I have some experimental data, in the form of a collection of curves with fairly little noise, ...
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How can I use Akaike's Information Criterion to compare two models of multi-peaked emission spectra?

I have several photoluminescence emission spectra that I am trying to fit curves to. The spectra each have a slight baseline and four peaks. The independent variable $x_i$ is wavelength (converted to ...