Tagged Questions

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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Explain log likelihood behaviour

(This question is related to a previous one I made, here) I have a set of 2D observations (measured data) of sample size $N$: $$O = \{(x_1, y_1), (x_2, y_2), ..., (x_N, y_N)\}$$ I also have a model ...
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1answer
41 views

Multiple Linear Regression Variable Selection

Using all possible subsets we consider the adjusted $R^2$, Akaike's Information Criterion (AIC), corrected AIC ($AIC_c$), and Bayesian Information Criterion. The model with the highest adjusted $R^2), ...
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Heckman's selection / switching model- selection variable as a regressor

I am conducting some analyses on the effects of membership and the membership rank on income. Everyone in the sample can choose to apply to join an organization. The membership is given upon ...
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2answers
95 views
+50

How to summarize knowledge about importance of variables?

Assume that we have 30 features (inputs) each of which can potentially influence the result (output). We try to use the available ("observed") mapping from features to targets to develop a predictive ...
0
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1answer
37 views

Good Literature about Problems with R squared

A question from a newbie. Recently, I was told that R squared or adjusted R squared can not used as a criteria to select a good regression model (model selection) due to, for example, overfitting . I ...
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2answers
45 views

Multiple linear regression, backward selection : Normality of the residuals?

I need to create a Multiple Linear regression model on those data explaining max03 T9 T12 T15 Ne9 Ne12 Ne15 Vx9 Vx12 Vx15 maxO3v !My data 1 My first intuition was to make a backward selection : ...
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0answers
21 views

Akaike information criterion for Cox proportional hazard models

I am conducting an analysis of survival data using Cox proportional hazard (CPH) models, to figure out what is the best model to use. The models I am comparing are non-nested. My plan is to compute ...
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0answers
14 views

Confidence intervals for the Log Loss metric for model comparison?

Quite a few Kaggle competitions have used or are using the Logarithmic Loss metric as the quality measure of a submission. I'm wondering if there are other ways besides N-fold cross-validation to ...
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0answers
4 views

Modelling for budget allocation experiment

I'm trying to figure out the best way to model the following process: An individual is given $k$ points and asked to fully allocate it between $m$ items on a menu. The items all share upto $n$ ...
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0answers
14 views

Comparing Two classification models using F1-score

I am trying to compare the results of 2 classifiers trained with SVM using the F1 score. Some papers that I have read and that do this have made me a bit confused. I have trained the 2 classifiers ...
0
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0answers
13 views

R: selecting appropriate fit statistics from GBM output

I'm using the gbm.step function in the dismo package in R to evaluate the contribution of three continuous variables on my ...
0
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1answer
55 views

Variable Selection for Logistic regression

I am performing logistic regression. I understand assumptions of logistic regression - Outliers, Multicollinearity. What i didn't understand how to select variables at beginning of model preparation. ...
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0answers
12 views

Sparse PLS: algorithm for variable selection and model fitting

In the spls package in R (based on the manuscript by Chun and Keleş [1]), there is a separate specification for the variable selection and fitting in the main function, ...
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15 views

How to select and compare a subset of models from a big set

I have a model-selection question, any help appreciated. I generate curves with known and well-defined properties. I have a set of 20 thousand candidate models that I use to do non-linear fitting on ...
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1answer
34 views

Deciding the Optimal Number of Factors [closed]

In practice, is there generally a difference between having 100 factors and 1000 factors in a model? Is there a well-researched 'upper-bound' to how many factors a given model should have?
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9 views

Should the rivals models include the same number of observed variables?

The question is about comparing models that include different number of observed variables. For example consider I have an 80-items questionnaire and I want to do confirmatory factor analysis (CFA) in ...
0
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0answers
9 views

AIC or similar selection techniques for Variograms?

I have a very basic question: how does one choose the "best" variogram? It is possible to fit different models to an empirical variogram, e.g. nugget, ...
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1answer
41 views

Which p-value to report in a comparison of different logistic regression models using marginal effects?

I am running logistic regression models to compare the impact of different indicators using Stata. As these comparisons may lead to false conclusion due to confounding and rescaling if log-odds or ...
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1answer
68 views

Simple model selection example in PYMC

I am currently experimenting with PYMC and I am trying out a simple example so that I start learning how things work (I am also a Python beginner, but an experienced machine learner). I have set ...
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0answers
6 views

How to specify that the model must include a particular independent variable in r package glmulti

I am trying to find the best model(lowest AICc value) using the package glmulti in r. This model must include TVIS as an independent variable but I am unsure how to specify this in the script. ...
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1answer
34 views

How to correlate categorical personality and music genre preference scores?

I'm currently a third year Biology student and I've annoyingly screwed myself over by not following the golden rule of stats, always know how to analyze your data prior to conducting the experiment. ...
0
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0answers
27 views

Model averaging with MuMIn. What's the mean of pvalue?

the summary() of a model.avg made with MuMIn in R, give a lot of interesting results, in particular model averaged coefficients (estimate, standard error, adjusted standard error and a z value with a ...
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0answers
28 views

Fitting a model to data for prediction - best choice for data

I have some data I need to fit a model to that can be used for prediction (interpolation). The data is summarized by the plot below. The black line is x=y. I want to be able to fit a model so as I ...
2
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1answer
51 views

c-index for parametric links in binary regression

I am conducting a binary regression using different sorts of parametric links (logistic, Pregibon, Aranda-Ordaz, ... see) and I would like to compare their predictive and classification perfomance in ...
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0answers
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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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2answers
50 views

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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0answers
5 views

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 ...
2
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1answer
43 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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0answers
6 views

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 ...
2
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0answers
30 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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0answers
30 views

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 ...
0
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1answer
16 views

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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4answers
490 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 ...
0
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1answer
41 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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0answers
9 views

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 ...
2
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1answer
91 views

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 ...
0
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0answers
16 views

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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0answers
13 views

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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0answers
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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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0answers
13 views

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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4answers
180 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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0answers
47 views

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 ...
0
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0answers
82 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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0answers
8 views

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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0answers
23 views

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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0answers
26 views

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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2answers
186 views

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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0answers
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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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0answers
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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 ...