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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10 views

Random effects for a continuous variable in a mixed Effects Models

I intend to fit a mixed effects model and all of my covariates are continuous. One of the covariates, say x2, is time (from enrollment) when the treatment was initiated and it is thought (by ...
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17 views

Error message eval(expr, envir, enclos) object 'X1914' not found [on hold]

I have been given two data frames x and y which I used to create two separate corpus ( corpora) Data frame X, has the dependent variable while Y, is the test set with similar variables and headings ...
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15 views

Real-time modeling of unbalanced repeated data for prediction?

The following data is about virtual driving tests (t1, t2, t3) either theory (T) or Practically (P). This data is stored from online system. I am trying to develop a real-time system that will predict ...
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16 views

scale of variance in Gaussian process

When performing Gaussian process regression, the variance at a prediction point is given by $var[f_*] = k(x_*,x_*) - k_*^T(k+\sigma_n^2I)^{-1}k_*$ (Equation 2.26 from GPML) The variance is not ...
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11 views

Technology Acceptance Models can be used for prediction?

I am planing to conduct a survey to predict the adoption of Mobile Banking service in the country where it was not introduced before. I am wondering whether it is possible to apply the Technology ...
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17 views

Scaling factor in R?

I am implementing a logit model on fraud detection data set which is having several attributes.Target column is truth or fraud.Data contains transaction amount is one of attributes.I generally scale ...
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40 views

Comparing Categorical Variables

What is your go to method to visualize relationships between categorical variables? At work, I find myself working with a discrete outcome variables quite a bit. When exploring data, I often want to ...
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59 views

What methods can I use to aid in modeling a smaller data set when I have a significantly larger data set with fewer variables?

I currently have a data set with about 4,000 rows. The current model I have established for it is not very good, and I am going to receive more data for about 150 of these points, and I'm hoping that ...
4
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1answer
49 views

Evaluate posterior predictive distribution in Bayesian linear regression

I'm confused on how to evaluate the posterior predictive distribution for Bayesian linear regression, past the basic case described here on page 3, and copied below. $$ p(\tilde y \mid y) = \int ...
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8 views

Should information crtieria be applied to training or validation data?

Information criteria for selecting models seem to be applied to training data in general. Could they also be applied to validation data to decide the most predictive and simple model, or is this ...
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1answer
15 views

Hosmer-Lemeshow test with weighted data

I am trying to perform Hosmer-Lemeshow test on weighted data (i.e. each observation in a data set has its weight). Unfortunately, I cannot find any literature on how to perform such test. Do you know ...
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6 views

Best approach for using predictive anlaysis to improve upon survivalist data

I previously have done alot of work on Arrhenius modelling in JMP using Censoring data variables. The factor that causes accelerated failure rates is Temperature and so the activation energy function ...
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1answer
32 views

Predicting value over time

I'm trying to predict the value of a variable after a specified number of days. I'm assuming it will change each day by a normally distributed random amount. For example, today the value is 10. Over ...
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24 views

Predicting customer churn

I'm trying to decide how to go about this problem. I have a large database of customers, both who have churned at some point, and who are current. I'm not sure how to create test/train sets from ...
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13 views

Leave one out cross validation error term interpretation

I have a dataset that involved 70 participants and 7 variables (1 y variable and 6 explanatory variable). I have used leave one out cross validation to assess the model and have resulted in an answer ...
0
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1answer
29 views

Applying randomforest algorithm (fit) on new data without recomputing the fit [closed]

I have a need to do realtime predictions for individual rows of data based on a previously computed randomForest algorithm. How can I run the "predict" command without recomputing "fit" on the entire ...
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25 views

Sample size needed to validate classification/prediction model

Dose any rule of thumb exist (or possible calculation) regarding sample size needed to validate an binary classification model. We have developed this prediction model for a medical condition and ...
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35 views

Why Binning Variables in Predictive Analytics?

Lot of discussion in CrossValidated focuses on optimal binning methods, binning example etc. But I am trying to figure out what are the scenarios that I have to bin variables whereas it's better idea ...
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15 views

Prediction model on hybrid data

I am currently working with a data set where I have both continuous, discrete and categorical (without any order) data. And I have to predict a continuous data. To be concrete, my problem is a ...
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2answers
51 views

Combination Forecast - Which models to pick?

Combination Forecasting can be produced by simply averaging different forecasts or employing more complex techniques (see Makridakis, 1989; De Gooijer and Hyndman, 2006; Goodwin, 2009; Pesaran and ...
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19 views

How to merge different predictive models training with different data sets?

Is there any good method to merge/consolidation different predictive models which were trained on different features but outputs the same goal. Example: Model 1 with features a + b + c (trained on ...
4
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1answer
33 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
3
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1answer
31 views

How to make a trained neural network “forget” an instance?

I am using neural networks for predicting the behavior of a dynamic system. A neural network is trained online using snapshots from the system's past. The system changes its state at irregular ...
3
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1answer
36 views

Weighted sampling as a way to eliminate specific source of variation?

I am facing a problem of predicting probability of an event given two correlated predictors where only one of them is of interest. Thus, I’m trying to eliminate one of them from the model while making ...
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31 views

Best approach to predict significant factors without any complete cases

I have a dataset that contains records of donors with various biographical info (city, state, zip, number of children) and the total amount they donated over 10 years. Some never donated and thus the ...
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7 views

Penalize a long-form panel linear regression prediction?

What is the recommended penalty, if any, for a long-form panel when calculating multiple linear regression $\hat\beta$ parameters or predicting single responses from (unobserved) values of $X_i$ in ...
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1answer
70 views

Explain “validation” process of repeated k-fold cross-validation?

My understanding is currently that the canonical repeated k-fold cross-validation (CV) process might do the following if $n=100$ observations in sample, $k=5$ folds, $i= 10$ iterations (see iteration ...
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1answer
16 views

Estimate linear regression paramaters with chain modeling for longitudinal data?

Within a frequentist, deterministic paradigm of multiple linear regression, is there a (standard) method to accomplish "chain modeling for panel data" in a way that avoids formal identity (and/or ...
0
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1answer
36 views

Change settings in the prediction model (caret package)

I am using the package caret and GBM method for my predictions. ...
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14 views

Which distribution should I better use to predict the response in {0,20} applying GBM? [duplicate]

I want to predict the response that is in {0,20}. I am using GBM to make the prediction. ...
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13 views

scoring/predicting for new observations

I have two data sets of variables where one of them - the new observations - has no dependent variable. The data set without a dependent variable has around 20 times the number of records. ...
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18 views

Can auto-predicted values ever improve linear regression?

You want to predict values of $y$ using a linear model of the following form: $ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \beta_3x_3$ $y$ is significantly dependent upon all three variables. ...
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16 views

On the prediction mean square error of a model

Suppose my model is $y_t = \alpha + \beta t + \epsilon_t$ the l-step-ahead prediction is given by $\hat{y}_{T+l | T} = a + b(T + l)$ where $a$ and $b$ are the OLS estimators of $\alpha$ and $\beta$. ...
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33 views

Should significance of each parameter be proved?

Suppose I want to prove the following linear regression, which represents a predictive model of $I$: $$ I = \beta_a A + \beta_p P + \beta_d D + \beta_s S + \varepsilon $$ Here $\beta$ are regression ...
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152 views

What is shrinkage?

The word shrinkage gets thrown around a lot in certain circles. But what is shrinkage, there does not seem to be a clear definition. If I have a time series (or any collection of observations of some ...
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22 views

R HoltWinters - Odd dip in predictions

So, I have this time series that tracks the daily number of applications to a graduate program. Each application period is 64 days - so for each period, you start at zero and it goes up until the ...
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37 views

using biomod2 package with continuous response variables

I am looking to correlate crop area with climate variables and then predict for future if suitability of crop areas will change/remain same under different climate scenarios (in line with species ...
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44 views

Discrimination between measurements made at different points in time

I would like to ascertain what variables discriminate best between experimental conditions in a repeated-measures experimental design. I have performed Repeated Measures MANOVA to determine whether ...
2
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1answer
79 views

How to score predictions in test set taking into account the full predictive posterior distribution?

I have three predictive models (regressions) which parameters are estimated by Markov Chain Monte Carlo. Predictions are made over a test set of size $N$. Since I compare the models under different ...
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45 views

Incorporating intraday data into end-of-day forecast

my target variable is observable intraday but I am interested only in EOD forecasts. I will denote the variable $\ y_{D,24}$ as the reading of interest for day D is ...
3
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57 views

Modelling flight delays with negative values

Modelling flight delays with negative values I am working on a model to predict whether a flight will be delayed. The data consists of some explanatory variables for flights from a specific airport. ...
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11 views

Comparing the impact of 2 independent variables on the dependent

I'm using a predictive modelling technique which has 2 parameters. I've performed a sweep of values for each of these 2 parameters, running each permutation of parameters 30 times as the technique is ...
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27 views

Incorporating systematic error in (spatial) predictive modelling

I have created a model (random forest) and withheld 20%. When I apply the model to the withheld dataset and check the residuals against the real values I can see there is a systematic error e.g lower ...
0
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27 views

How can I use the output of KODAMA to predict unknown data points?

I can use KODAMA to create a model that classifies input data into two groups by setting the W vector to indicate the group and fix to a vector of all ...
0
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1answer
29 views

One-Step ahead predictive likelihood for time series forecasting

I am still new to Bayesian forecasting, so I am hoping to get some clarification on a simple concept (by the sounds of it). Suppose that we are interested in forecasting some time series one-step ...
0
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1answer
16 views

Predicting Arrival/Departure of butterflies

I don't have a solid background in statistics. I am double checking with you on a phenomenon I am trying to study. we are doing a study of some very rare species of flowers. We are putting them in ...
0
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1answer
19 views

train multiples observations from the same person in caret

I have data where persons were give four different tasks under three different conditions (intensities). The data looks like this: ...
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1answer
31 views

What predictive models allow me to make new predictions on a series in constant time, without needing to recompute previous ones?

I'm a software developer working on a system that stores thousands of independent metrics, each with several tens of thousands of timeseries data points. We'd like to make predictions about where the ...
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0answers
70 views

Overfitted Cox regression

I'm trying to compare the prediction abilities for death of two new biomarkers using a cohort of 173 patients. My problem is that I have only 31 outcomes and my baseline model (the one without any ...
2
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75 views

What models allow the study of the relation between two sets of variables?

Usually when you think of a model you have a single target variable which you associate to explanatory variables to try and find a pattern. In contrast with the idea of having a single target ...