Predictive models are statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain a phenomenon mechanistically. As such, predictive models place less emphasis on interpretability and ...

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Do models with multiple covariates and single covariate models differ from each other?

I have a binary response variable with 9 predictor variables. Lets denote the predictors $A, B, C, D, E...$ Suppose I run a model $y_i = \beta_0 + \beta_1 A + \beta_2 B + \beta_3 C + \cdots$. The ...
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5 views

Interpretation of standardized beta coefficient estimates and use within the exponential formula for prediction purposes

I'm working on a data set where I plan to use logistic regression to evaluate non-random habitat selection for a wildlife species. My dependent variable is 1 = used location by an animal and 0 = ...
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16 views

Root-Mean Squared Error for Bayesian Regression Models

I'm trying to get a sense of my prediction errors for a Bayesian regression model and I was using the Root-Mean-Squared Error. My question is, since are predictions are stochastic, would it make ...
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5 views

Differences between cross validation and bootstrapping to estimate the standard error of the AUC of a given ROC curve

I know there's been some discussion on differences between CV and bootstrapping for estimating out-of-sample prediction error of a classifier. For example, in here (Differences between cross ...
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5answers
2k views

Should parsimony really still be the gold standard?

Just a thought: Parsimonious models have always been the default go-to in model selection, but to what degree is this approach outdated? I'm curious about how much our tendency toward parsimony is a ...
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32 views

Statistical technique to apply in Airline Industry [on hold]

I am working to Airline Industry data where frequency of travelers in a year is up to 1. I need to increase the frequency of the travelers using some predictive modelling approach so that campaign ...
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29 views

Tennis Analytics: How to Build Model Predicting Player Service Point Win %

I have collected a large amount of tennis match data including player names, court surface, player ranking points at time of match, handedness of player, point by point breakdown of match etc. I ...
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18 views

Use Logistic Regression Literature for Logit Discrete Choice Models

I'm currently developing a binary logit Discrete Choice Model (DCM) in the context of my thesis. Obviously, I want to develop the model following academic standards. A few questions have been arising: ...
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When can correlation be useful without causation?

A pet saying of many statisticians is "Correlation doesn't imply causation." This is certainly true, but one thing that DOES seem implied here is that correlation has little or no value. Is this ...
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25 views

Appropriate predictive model for two random time series with serial correlation

Say I have annual observations of the temperatures at the North Pole and South Pole for many years. I want to build a model that given the South Pole temperature for the current year and all prior ...
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2answers
62 views

Random Forest - Numeric and Dummy Variables together

I am trying to create a logistic regression model and a random forest model on the same data to predict probability of default. For the logistic regression model, I have created some dummy variables ...
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8 views

Predicting responses to test-data variables that lay far outside model training-data variable range

What can one do to improve model predicting accuracy when using test data that has a variable with magnitudes far outside the range of that variable in the training data? Example: This question ...
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14 views

Methodological test for choosing 'worse' models that make 'better' (more realistic) predictions?

I've run 4 models (simple LM, quadratic model, GLMM, and GLMM with quadratic) to predict tree age (age) from tree diameter (D) for each of 42 species (SPEC). The diameter data has all been log ...
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12 views

Interpolation of Data Value using Optimized Weighting of Its Features

I have a question regarding "Interpolation" / "Prediction" of a value. Assuming we have a data set $ { \left\{ \left( {x}_{i}, {y}_{i} \right) \right\}}_{i = 1}^{N} $ where $ {x}_{i} \in ...
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1answer
35 views

Regression model for edge-sensitive data set

I have data sets in which important information is allocated in the edges, which are also very sensitive to inaccuracies. I would like to find a regression model based on edge recognition that brings ...
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67 views
+50

Relative variable importance for Boosting

I'm looking for an explanation of how relative variable importance is computed in Gradient Boosted Trees that is not overly general/simplistic like: The measures are based on the number of times a ...
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1answer
28 views

Beyond least squares: how to choose a predictive model or algorithm? (reference request)

There are dozens of algorithms one can use to build a predictive model. What books or studies exist that can help one determine which algorithm to use? Elements of Statistical Learning spends a lot ...
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1answer
37 views

Forecasting monthly time series with known periodicity and a known driver

For 2004-2014, I have monthly measurements of my outcome of interest - some kind of physical exposure - for a collective of many thousand persons. The main determinant for the average exposure level ...
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30 views

Building a Predictive Model

I'm inexperienced and confused in statistics, so I need help. I have a data table, values are temperature, particulate matter(PM), and vegetation indexes. And idea is that when PM increases, ...
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5 views

Bayesian approach for comparing the predictability of different datasets for another

Suppose I have three datasets A, B and C with not necessarily the same amount of data. Now, I want to know whether dataset A or dataset B is better in predicting C. I thought of using a Bayesian ...
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7 views

Unable to predict using bart() {BayesTree}

I used bart function from BayesTree library to build a model on my training data. It fits my training data very well. However, I'm unable to predict for the test set and check its performance. ...
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31 views

Regression to Predict a 'Discrete' Proportion

I am to build a model $y_i \sim f(X_i, n_i) + error_i$. The regressor $y_i \in [0, n_i]$. $y_i$ and $n_i$ are positive integers. Each observation $i$ has a different known $n_i$, and $n_i$ varies ...
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10 views

Clarification on Prediction with a Regression Model using Centered Variables

As I understand it, for a regression model, centering the variables around their means can be helpful since it makes the intercept term the expected value of $Y_i$ when the predictor variables are set ...
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20 views

Model selection in the classification problem with costly information

Let's assume we have a $X_T$ matrix of $N$ variables and $Y_T$ available for training a model to solve classification problem for variable $y$. Normally, we can use all $N$ variables for training and ...
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18 views

Prediction of one time series from another with multi-dimensional predictors

I have the following problem (I am a newbie to the field so my apologies if this is something standard). I have two time-series with the same (categorical) predictors (x1,...,xn), n is approximately ...
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28 views

Can we improve the Multiple R-square(coefficient of determination) value for a dataset of a linear regression model?

Here is what i am doing. I am building a logarithmic model in linear form based on the correlation between two variables shown in the graph! lm(y~logx,data=logdata) -- i have only one predictor and ...
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11 views

Quantifying Independent variables for Multiple linear regression

I have 3 independent variables (factors) that prompts banks to close.one variable is financial pressure, 2 variable is drop in donor charity and 3rd is for profit conversions. Now how do I quantify ...
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32 views

Backtesting in neural network field

I'm new to the neural network field and I would like to understand how one can backtest a neural network trained with backpropagation methodology. Particularly, I have a time series dataset and I ...
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37 views

Is there any algorithm that can predict multi-variables(response variables) based on one independent variable

let me ask the question in detail with an example -- I have a historical dataset with columns(a,b,c,d,e,f,g) Now i have to predict (b,c,d,e,f,g) based on the value of 'a' and all the variables are ...
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42 views

List of nlme models

I am trying to find a list of models that nlme provides. I am completely new to this area and finding it hard to get a comprehensive list of models that nlme provides facilities for. I have tried to ...
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14 views

Forecasting with Dynamic values using R [migrated]

I have a Json object : {"tcDetails":[{"project_nm":"abc","id":"1","n_tc":"32","TC": [{"29/06/2015":50,"30/06/2015":45,.....}] {"level":[{80,85,90,95}]}]} ...
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13 views

Forecasting values along with corresponding years [migrated]

I have a sample data set (named as s3) in the following manner: ...
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9 views

Missing at source data and predictive model

I have multiple sources of data, and each comes with its own set of observable cahracteristics. Most are common between all sources, but some sources have extra information that is useful, but not ...
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29 views

Multivariate multiple linear regression model

I have 2 response variables (Y1, Y2) and some independent variables. I need to predict both Y1 and Y2 using the same set of predictors.In other words I need to fit the model: $Y=XB+E$ where: $Y$ has ...
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8 views

Regression model with percentages

I have a dataset with 5 dependent variables ($y_1$,$y_2$,$y_3$,$y_4$,$y_5$) and some independent variables. Each dependent variable is a percentage (so it goes from 0 to 100). The sum of the 5 ...
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19 views

How to interpret the R output of anova.coxph

The likelihood ratio chi^2 test is the gold standard for comparing nested Cox models. However I am not sure how to interpret the R output. ...
2
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1answer
43 views

Choosing a sample rate for GBM models

I've created several GBM models to tune the parameters (trees, shrinkage and depth) to my data and the model performs well on the out-of-time sample. The data is credit card transactions (running into ...
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1answer
23 views

Manually scoring logistic regression model in SPSS? [closed]

First off, I'd like to apologize for my cluelessness, but I've come across a problem that I honestly have no clue how to circumvent. My programming skills are extremely limited, and my company uses ...
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27 views

Uplift model with a continuous outcome?

Does anyone know any good packages (preferably in R/python) or references that are specifically about building the uplift model with a "continuous" outcome? I've used the upliftRF from R and made it ...
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28 views

How to identify important independent variables for a dependent variable?

I have a dependent variable (DV) and about 200 independent variables (IVs). I want to understand which of the 10-20 variables are important for this DV. I could do: PCA - However it'll only tell me ...
3
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1answer
63 views

ROC curve for two-sided cut-off

I am very, very confused about ROC curves. I have a Bayesian model which outputs a prevalence on a continuous scale between 0 and 1. I have a classification I would like to use that classifies that ...
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0answers
11 views

reduced multilabel-dataset performance evaluation

Assume a multilabel problem with given ground truth, where each training instance can have one or more of 3 labels A,B and C, e.g: ...
1
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1answer
35 views

optimal sequential sampling in gaussian process models

Let's say we have a one dimensional dataset of 24 points along with their responses. I am reserving three boundary points for testing (i=1,23,24) and i am fitting a Gaussian process model based on a ...
7
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200 views

Should predictive accuracy or, alternatively, minimizing the MSE, be reconsidered?

Ever since Breiman, maximizing predictive accuracy has become a predictive modeling gold standard, of sorts. That it has evolved to this status is understandable: it can be "optimized," is easily ...
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1answer
38 views

What is the link between the logit and the probability of a binary event?

Reading about logistic regression model, I wondered about the link existing between the logit (or $log\frac{p}{(1-p)}$) and the probability of an event defined as binary by assumption and modeled by ...
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0answers
8 views

How to give less weight to past history when there are fewer past observations in a panel regression?

I'm interested in how to structure a panel regression so that units that don't have as many past historical observations don't have their predictions as highly influenced by their past data. For ...
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11 views

Determining contribution of histogram bins to a total

I have a situation where data in a histogram contributes to a final total numerical value. The weight of the contribution of each of the bins towards the final value is unknown, but a selection of ...
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189 views

Clarifications regarding reading a nomogram

Following is a nomogram created from mtcars dataset with rms package for the formula: mpg ~ wt + am + qsec The model itself seems good with R2 of 0.85 and ...
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40 views

Need a method for determining variable groupings in R

I am using R and trying to group one of my variables into larger groups so they have credibility. I have been manually setting each factor of the variable as the reference level, looking at all other ...
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18 views

Comparing probabilities of two predictive models

Someone has already asked this question. But it is not answered. I have 10 logistic regression models for 10 different product categories. Then i need to come up with the best product to be offered to ...