Questions tagged [explanatory-models]
Models created to explain a response (as opposed to simply predict it). This is generally understood to imply models of causal processes, or to test hypothesized causal relationships.
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Is a predictive model the best way to generalize about my current data?
Suppose I have a customer dataset. My independent variables are various types of customer attributes (age, where they live, gender, price of the item they are potentially buying) and my dependent ...
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4answers
579 views
On George Box, Galit Shmueli and the scientific method?
(This question might seem like it is better suited for the Philosophy SE. I am hoping that statisticians can clarify my misconceptions about Box's and Shmueli's statements, hence I am posting it here)....
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Application of GAM on large dataset
I was suggested that my questions were too broad. As I commented below, I have nearly a million data points and perhaps a hundred variables. This may be a very basic modeling question: I am curious to ...
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1answer
58 views
What are the most important/seminal/popular methods in the field of interpretable ML?
As I've been trying to get a grasp on the field of interpretable ML I have encountered many interesting papers and methods, but I lack the perspective to determine their importance to the field as a ...
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1answer
43 views
In scale adaptation which should be done first? CFA or Reliability Analysis
I am adapting questionnaire cross-cultural. Questinonnaire has 11 item and two factor. I want to confirm the structure
1 item's corrected item total correlation is <.20. Should this item be ...
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1answer
22 views
Is this an appropriate way of modeling the scores in a round-robin sports league?
I want to model the outcome of matches in a round-robin sports league based on which home team is playing which away team across several seasons.
Let's assume a league with four teams ...
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2answers
130 views
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 ...
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0answers
207 views
Dealing with unbalanced data in an explanatory model with decision tree
I am doing binary classification with decision tree, and it aims to find out what features matter the most with the data we have, so I need interpretability more than predictability. It is like ...
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2answers
93 views
Standardization and explanatory variables of different domains in Multiple Regression
There's many questions on related topics but I have been unable to find one that precisely answers my question.
Let's say I'm performing a regression on multiple predictor variables $x_1...x_n$ for ...
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How does LIME compares with Mutual Information?
So, I was wondering how LIME's linear model approach compares with other explanation metrics, in special, with Mutual Information?
For those unfamiliar with how LIME works:
Choose the instance you ...
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3answers
163 views
General approaches and techniques for developing good explanatory models for nonlinear data
Various recent efforts of mine on modelling some data through logistic regression have been... not successful. While there is still more data to look at, I've been wanting to explore nonlinear ...
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1answer
72 views
Quantifying explanatory potential
Suppose I have a random variable $T_j \sim Bernoulli(p_j)$ where $logit(p_j) = \theta x_j + \epsilon_j$ and where $\epsilon_j \sim \mathcal{N}(0,1)$. Suppose further that $\theta = 0.018$ and that I ...
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Minimizing bias in explanatory modeling, why? (Galit Shmueli's “To Explain or to Predict”)
This question references Galit Shmueli's paper "To Explain or to Predict".
Specifically, in section 1.5, "Explaining and Prediction are Different", Professor Shmueli writes:
In explanatory ...
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4answers
7k views
Is MSE decreasing with increasing number of explanatory variables?
I am wondering, if there is a negative correlation between Mean Squared Error
\begin{equation}
MSE = \frac{1}{n} \sum (\hat{Y}_i - Y_i)^2
\end{equation}
and the number of explanatory variables. ...
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0answers
569 views
How exactly does ridge regression helps in the case of multicollinearity?
I understand the reasoning behind ridge regression: we include some bias in the model in order to reduce the variance of the regression coefficients. My question is, why would we want to do that?
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Purpose of leave-one-out cross-validation in descriptive modelling
I refer you to Breiman's paper Statistical Modeling - A Tale of Two Cultures where he illustrated some examples of descriptive modelling. Under section 11.1, 100 runs of regression were performed, ...
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1answer
81 views
Is inference based on full (global) regression model appropriate?
Is inference based on a full model appropriate, and if so, in which circumstances?
Suppose you are interested in the potential relationship between a response variable and several candidate predictor ...
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288 views
Should I use unpenalized logistic regression, lasso or ridge for explanatory modelling?
When using logistic regression for predictive modelling, the choice between 'standard' logistic regression vs ridge vs LASSO versions of logistic regression seems relatively straightforward - just ...
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1answer
187 views
How to test predictive ability of an explanatory model?
As a part of my research I create an explanatory negative binomial regression model. Now, I want to show this model can also have predictability power. I don't want to compare my model with other ...
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3answers
7k views
Predictive power vs. explanatory power of statistical models
Is it possible for a statistical model to have explanatory power but no predictive power?
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55 views
Assess a model building technique
I am confused about a certain model building technique that seems to exist, at least in practice (I am not sure whether it has its place in textbooks). Question 1: I wonder under what conditions or ...
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1answer
353 views
LASSO for explanatory models: shrinked parameters or not?
I'm conducting an analysis where the primary goal is to understand the data. The dataset is large enough for cross-validation (10k), and predictors include both continuous and dummy variables, and the ...
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0answers
110 views
A bunch of different types of variables (their combination also important) explaining one variable - which method?
I have a dependent variable - how much land does a household cultivate out of total in their possession. The answers are categorized in 3 different groups (1 - 70% - 100%, 2 - 40 - 70%, 3 - less than ...
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1answer
880 views
Prediction vs. Explanation and its Effect on Statistical Methods [duplicate]
In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating:
My ...
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1answer
272 views
Predictive modeling techniques for in-sample rather than out-of-sample prediction?
Is it appropriate to apply predictive modeling variable selection and shrinkage techniques (for example, ridge regression or lasso) for in-sample prediction rather than out-of-sample prediction? ...
