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Questions tagged [parsimony]

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7 votes
3 answers

Is parsimony crucial for statistical inference?

This question is based on using a regression for statistical inference (not prediction). I have conducted hierarchical (logistic mixed effects) regression. The first model includes the predictors of ...
SilvaC's user avatar
  • 512
2 votes
1 answer

Goodness of fit of structural equation modelling

I am currently working on a structural equation modeling project using the lavaan package in R. The model satisfied all the goodness of fit tests (GFI, AGFI, CFI, ...
Suba's user avatar
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3 votes
1 answer

Difference between Parsimonious model vs Optimal model

As per my understanding, parsimonious regression model is the model that has less variables but with those variables I can describe the data best. Is it so? Then ...
user avatar
3 votes
1 answer

Structural complexity versus ontological complexity

From the article Another contentious aspect of the razor is that a theory can become more complex in terms of its structure (or syntax), while its ...
Single Malt's user avatar
4 votes
1 answer

Akaike Information Criteria applied on Random Forest

I am implementing a Random Forest model for predicting a variable "A" which is function of other 4 variables: $$A = f(B,C,D,E)$$ I developed a good RF model (i.e. high accuracy, good ...
cdmon's user avatar
  • 41
0 votes
0 answers

Why do contrasts influence singular fits with mixed models?

I've fit a linear mixed effects model to some data in R with afex::mixed. I'm interested in the fixed effect and have no expectations for the random effects ...
user72716's user avatar
  • 219
1 vote
0 answers

If two models have similar predictive power, why should we prefer the one with fewer parameters?

Was thinking a bit about model selection earlier, and I ended up getting hung up on the question: “If two models have similar predictive power, which model should I select?” For example, we often ...
Louis Cialdella's user avatar
4 votes
1 answer

LASSO versus likelihood ratio tests for variable selection

LASSO regression penalizes coefficients in regression to at most zero. Likelihood ratio tests tells us whether the nested or full model is better. I used likelihood ratio tests during regression ...
user avatar
0 votes
1 answer

Assessing loss of different parsimony levels (Cox Model)

I have a Cox Proportional Hazard model with 6 covariates to determine OS. I am now trying to simplify this model by taking some of this covariates down. This is intended for a wide audience so I'm ...
N00b's user avatar
  • 23
4 votes
0 answers

Is there a measure of "complexity" for linear/nonlinear model terms?

My apologies if this is grossly misunderstood or mis-worded, but I've been mildly bugged by a question to which I've not found a satisfactory answer. I can't say that I have seen a discussion about ...
Chris Moore's user avatar
7 votes
2 answers

Does cross validation say anything about parsimony?

Suppose I had a set of models that all attempt to explain some phenomena. According to a sensible—and appropriately cross-validated—performance metric, all of the models perform comparably well. The ...
Matt Krause's user avatar
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