Linked Questions
13 questions linked to/from Minimizing bias in explanatory modeling, why? (Galit Shmueli's "To Explain or to Predict")
37
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4
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3k
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Are inconsistent estimators ever preferable?
Consistency is obviously a natural and important property of estimators, but are there situations where it may be better to use an inconsistent estimator rather than a consistent one?
More ...
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27
votes
3
answers
7k
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Paradox in model selection (AIC, BIC, to explain or to predict?)
Having read Galit Shmueli's "To Explain or to Predict" (2010) and some literature on model selection using AIC and BIC, I am puzzled by an apparent contradiction. There are three premises,
AIC- ...
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12
votes
3
answers
2k
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Regression and causality in econometrics
In regression in general and in linear regression in particular, causal interpretation of parameters is sometimes permitted. At least in econometrics literature, but not only, when causal ...
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8
votes
2
answers
1k
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What is the relationship between minimizing prediction error versus parameter estimation error?
With the advent of statistical learning techniques, people are talking a lot about prediction error, while in classical statistics, one is focusing on parameter estimation error. What is the ...
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3
votes
4
answers
576
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What is the main purpose of Feature Selection?
I have a small medical dataset (200 samples) that contains only 6 cases of the condition I am trying to predict using machine learning. So far, the dataset is not proving useful for predicting the ...
- 133
20
votes
2
answers
536
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Can regularization be helpful if we are interested only in modeling, not in forecasting?
Can regularization be helpful if we are interested only in estimating (and interpreting) the model parameters, not in forecasting or prediction?
I see how regularization/cross-validation is extremely ...
- 4,212
10
votes
1
answer
301
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Statistical Learning. Contradictions?
Currently I am re-reading some chapters of: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (Springer, 2015). Now, I ...
- 4,896
6
votes
1
answer
2k
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Endogeneity in forecasting
I know that omitted variable bias isn't a major problem in forecasting, but are other endogeneity issues (such as simultaneity or measurement error) going to be a problem if I am only interested in ...
- 61
2
votes
2
answers
1k
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Inference, Prediction, & Model Fit?
I have a background in statistics (for social science), but I am confused about the ways in which Data Science textbooks (in particular, An Introduction to Statistical Learning and Practical ...
- 41
1
vote
1
answer
711
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Can "Prediction" and "Inference" be used Interchangeably? [duplicate]
Within statistics, I have heard that almost all analysis can be broken into two general classes:
Prediction : E.g. Statistical Modelling, Machine Learning
Inference
I have seen the term "...
3
votes
1
answer
419
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endogenous regressor and correlation
In a widely cited paper by Antonakis et al. (2010), they mention:
If the relation between x and y is due, in part, to other reasons,
then x is endogenous, and the coefficient of x cannot be ...
- 497
2
votes
0
answers
445
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Machine Learning with few observations
Is common to say that Machine Learning techniques represent are purely data driven methods, and them are effective only if we have a large amount of data. I focused here on supervised/predictive ...
- 4,896
2
votes
2
answers
171
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Neural Network vs regression in prediction
I collected a sample of 600 observation (time series data) with 100 predictors variables in order to predict another one. I want to use some prediction models but I know that, unfortunately, ...
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