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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Difference between prediction in R and SQL

I'm working on a prediction model for a continuous variable (amount of medicine injected) .I use R for modeling.My project flow is to multiply the prediction of a glm (logistic regression) model that ...
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20 views

Modeling Counts With Small Observations

I am new to Cross Validated SE so I am going to try and formulate my question to the best of my ability. I have a large data set that contains $5$ different fields. The fields are ...
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23 views

Model specification with Deflators: methodological question on forecast model

I am trying to build a model to predict one year ahead Earnings per share $(t+1)$ based on variables in year $t$. I’ve seen a lot of models in practice that use the following methodology: ...
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18 views

Predicting missing values in data [on hold]

I have a dataset that has some missing values. How can I use basic statistics or mathematics to predict the missing values? Right now, I am considering the value before the missing value and the ...
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18 views

Out-of-time testing (basic question)

I understand the importance of out-of-sample testing, but could you tell me why I should (or shouldn't) do out-of-time testing ? The only use that comes to mind is if the predictive model applies to ...
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27 views

Using OLS for Model Selection and Prediction - Heteroscedasticity Issue

I am new to regression and having problem in solving Heteroscedasticity in OLS. Have done lots of homework and test before seeking your advice. Sharing the background and what I have done to solve the ...
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22 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? ...
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11 views

Calculating upper and lower confidence limits on a population estimate derived from multiple point estimates

I am generating a river reach population estimate for a freshwater mussel by summing point estimates made across a gridded point network (within the reach) using a generalized linear mixed model ...
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152 views
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How to evaluate fit of a logistic regression

I have a set of data points, which exhibit a solid linear correlation $r\approx 0.9$. I am basically plotting population in certain areas against the number of occurrences of a certain phenomenon (so ...
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33 views

Confusion between caret randomForest predict() results and reported model performance

This question seems related, but the consensus was that the issue had to do scaling the data, which I do prior to training, so I don't think that's the issue: Issue on prediction with FinalModel of ...
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20 views

Predict/impute one cell of matrix using all other cells

The question: I want to predict/impute one missing cell of a matrix using the contents of all other cells. Anyone have ideas on how to do this? The context: The matrix is n people's responses to m ...
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26 views

Estimating a distribution from a dataset with multiple parameters

How would you go about solving the following problem? You're an insurance company who writes workers compensation policies. You want to build a probability distribution for the number of annual ...
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31 views

Forecasting agricultural commodity prices with R

I would like to create a predictive model in order to forecast the price of an agricultural raw material. I got time series for the prices and the production of this raw material, and also for the ...
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33 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
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3answers
127 views

Best method to predict binary outcome with multiple records per subject

I am interested in building a model to predict the binary outcome, retention (1 - retained; 0 - not retained) with various potential predictor variables (either continuous or categorical). With that ...
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25 views

Applying the Akaike Information Criterion to Data

I have some variables that I would like to run regressions on, to create a model, but I am unsure about how to actually AIC (or the BIC). Unfortunately I have not yet taken a mathematical statistics ...
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48 views

Anomaly detection using exponential weighted moving average

I would like to detect anomaly using exponential weighted moving average. I don't have series of data points. All I have is EMA(t-1) and the data point of the current time(t) DP(t). From these data, ...
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1answer
32 views

Definitions of Prediction vs. Predictor

I am writing an article which includes discussion of the MMSE estimator of the posterior predictive distribution. Since I use this term quite frequently, I was considering referring to this estimator ...
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18 views

What are some multivariate models with feature interactions

I have dependent variable matrix $Y_{i,j}$ and feature matrix $X_{i,k}$. My objective is to predict each element of the vector $[y_{i,0},...,y_{i,J}]$ by using new observations of the features, ...
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26 views

random forest and prediction

I am building a random forest model to make predictions. Response variable is treated as continuous but not really continuous, e.g., integers from 0 to 10. I have problems in constructing ...
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29 views

Possible inferences from a graph pattern

So, I had a weighted dynamic graph having info about 10 consecutive timesteps ( basically 10 files ). Now, I had to mine out patterns in the weight and structure of the complete graph. I did that. The ...
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39 views

Acronyms to use for Bayesian posterior predictive distribution estimators

I am considering writing an article that discusses the Bayesian MMSE and MAP of the posterior predictive distribution. I was wondering if there are acronyms that have been used so that instead of ...
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41 views

temperature prediction algorithm

I found an interesting problem in a contest on temperature prediction: https://www.hackerrank.com/contests/expansion-challenge/challenges/temperature-predictions It is not about forecasting the ...
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43 views

Taking into account Bayesian model uncertainty

I recently received a review of a paper from a Bayesian Statistics Journal. The Associate Editor wrote this mini-review (quoted below in full). The paper is talking about Bayesian modeling of DNA ...
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22 views

Recognize spatio-temporal patterns correlating with events

I am trying to recognize a spatio-temporal pattern in my spatio-temporal input sequence X. The occurrence of the pattern is roughly temporally correlated with another event E1. For example I have ...
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57 views

Creating a model for prices including supply

I'm working on modeling secondary market ticket prices for sporting events, but the issue I'm running into is that the model (a linear regression) assumes that more season ticket holders and more ...
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53 views

Forecasting: Different Model for 1 month, 2 month, 6 month forecasts?

I'm still trying to expand my statistics and forecasting technique knowledge. Right now I'm forecasting seasonal contact patterns, so the simplest model I can understand with seasonality is a ...
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119 views

Are world cup predictions testable?

As of today, dozens of soccer world cup predictions exist, some more complex, some more elegant, and most of them predict every nation's "chance" of winning a particular match/ the cup. As I am ...
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30 views

spatial-location/time-series prediction models

How efficient it is to build predictive model. However, every crime is dependent on three factors: Time, Spatial Location and people behavior. Statistically, we can't measure people behavior (we ...
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Building a flexible predictive model to put together fashionable outfits

I have a rather open-ended question that I'm hoping people can help me brainstorm for. Imagine you wanted to design an application to help you put together outfits from your closet. To simplify the ...
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39 views

Feature selection: permutation test Vs deleting a variable

In feature selection for predictive models, it is usually applied a permutation test. In this test, all the values of one variable are randomly permuted and the prediction accuracy is extracted for ...
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189 views

questions that can be asked for linear regression and logistic regression

I am preparing a potential interview, which can involve detailed questions on linear regression and logistic regression. Besides going over the textbook, could you share some possible interview ...
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29 views

Individual slopes for many zip codes over time

I have a dataset where I am interested in calculating a slope for each observation / row. I have dependent variable $Y$ that is continuous. Every $Y$ is unique to a zipcode. and my independent / ...
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42 views

Machine learning from implied variables

I have a situation where we are detecting anomalies based on data implied from the table data. As an example, I have data on registered individuals spending time on the portal. Based on this, I have ...
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10 views

How to calculated the accuracy from two graphs without equation. but i have all of their values?

What is the good or best method to calculate this. I have two graphs like this The black line is real value. red line is the value from estimator. I can call every Y-axis value from every X-axis ...
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128 views

Predictive Modeler: How can learning Python and/or Java benefit me?

On a daily basis, I build predictive models (namely, logistic regression and credit scorecard models) using fairly large datasets (typically ~500k records and ~1k candidate variables) to predict ...
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On which data should the lift be calculated i.e. Training set or Test set and why?

On which data should the lift be calculated i.e. Training set or Test set and why? What does the lift value 115% mean
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62 views

What type of predictive analysis should I use? [closed]

I have a pure math background with knowledge of basic statistics (random variables, inference, etc.) but am new to predictive modeling. Here is my situation: I have a bunch of independent variables ...
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1answer
39 views

Is it possible to analyze a problem using graph if number of features is greater than 2?

By graphing, you can see if the data is linear or not, basically you can analyze the structure of your data easily and you can then decide which model to use. If you have, say, one feature, you can ...
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24 views

How to pedict spatio-temporal events? [closed]

My data set consist of a large set of events. Each event occurs at an arbitrary location and lasts for an arbitrary but limited amount of time. Since I have no experience with spatio-temporal ...
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1answer
39 views

Estimate event probability from yes/no data

I need to predict (estimate) probabilities of (rare) events when the training data only contains the yes/no indicator. I.e., my target (dependent) variable is binary (logical). What I need is not ...
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1answer
43 views

How to calculate the next year's January hourly forward prices for an asset given the historical hourly prices?

I have an exercise given to me during the recruitment process which asks to calculate the next year's January hourly forward prices for an asset given the historical hourly prices of that asset and ...
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16 views

Update probabilities from a classifier with a priori probabilties

I am trying to build a model to correct the class probability given by a classifier with a priori probabilities. Here are the probabilities that I have for that model: predicted probabilities from ...
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36 views

Variance of predictions from lme4 models at existing levels?

I know that this has been widely discussed, but even after having read many discussions on this matter, I'm still not sure if I'm understanding properly. So I have a dataset of studies reporting ...
2
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1answer
84 views

Logistic regression with time series predictor data

I like to know if we can model binary outcome with time series predictors. For example lets say Y is binary. $X_1, X_2, X_3,...,X_n$ is the same predictor variable but is a historical snapshot over ...
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2answers
43 views

Is highly correlated factors in a prediction model a problem?

I have built a logistic regression model with two or more highly correlated factors. I did this by doing a bagging procedure. In my understanding having highly correlated factors in a prediction ...
2
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2answers
185 views

When do you know when modeling something with a binomial distribution is better than a Poisson?

I was trying to understand when its better to model some random variable as being distributed by a Poisson Distribution or when being modeled as a Binomial Distribution. I was reading the following ...
2
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1answer
119 views

What does this residuals versus fitted plot mean about my model?

I have a model that attempts to predict a nation's quality of life index by it's moral indifference to contraception and moral rejection of gambling. Initially the model contained several predictors, ...
3
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2answers
148 views

Statistical method for modelling of disease incidence for NGO

I am trying to provide help to an NGO in the non-profit sector that is running a disease screening program: The program visits thousands of villages a year. A village has a population (on average ...
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122 views

Choosing correct C and g parameters for libsvm

libsvm 3.18 Features: 10 I have used following, parameter range: ...