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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19 views

difference between confidence interval and prediction interval in the context of regression analysis and predictive modeling

When building prediction models, I always see the following concept 1) Confidence interval for regression model 2) Prediction interval 3) Confidence interval for predicted value I can understand ...
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16 views

Machine Learning Predictors Evaluation Using R

I've bee using R for predicitve analytics and here is issue: I'm trying to predict the species (categorical variables E1, E2, E3 and E4) of an animal using as predictors a set of nominal (NO1, NO2, ...
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8 views

Autoregressive model - predictive power

I have estimated a VAR (vector autoregressive) model on credit growth in STATA. I want to test its predictive power by comparing its estimated credit growth to observed credit growth (correlation ...
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1answer
33 views

Machine learning algorithm to predict next user's destination

I'm searching for a way to formulate my problem as a machine learning problem. Suppose I have a history of user's locations, and I want to predict his next location, similar to how Google Now does it ...
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15 views

Sound waves are they amplitude or frequency modulated? [on hold]

This question has two perspective one for sound generated by humans and other generated by electrical speakers. From my guessing when we speak we alter both amplitude and to some extent frequency but ...
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47 views

Linear regression with log dependent variable

I have the following regression: $log(Y) = \alpha + \beta X + \epsilon$ with $E[\epsilon] = 0$ and $var(\epsilon) = \sigma^2$. There is no assumption on the distribution of the errors $\epsilon$. In ...
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30 views

Trying to find a classifier that will give me probability predictions between 0-1 in weka

This is the first time I've done any sort of predictive modelling and I think I've really confused myself. I have a training set of data with a column at the end that has either a 1 or a 0 in it. ...
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1answer
29 views

Are insignificant variables included in calculation of predicted probabilities?

When calculating the predicted probabilities in a logistic regression model, do we consider all the variables or just the significant ones? For eg: Let's say my model has: dependent variable Y and 3 ...
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16 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
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6 views

how do i differentiate between alternative specific and alternative invariants using sas?

Okay so my level of expertise in statistics is fresher/rookie. I am trying to predict the the outcome of categorical choices for a product category y (3 choices or 3 flavors of cereal for example). ...
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17 views

input variables with different order of magnitude [duplicate]

I need to build a prediction model based on a data set with 5 different independent variables. The data set looks like as follows. The variables in col4 and ...
1
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1answer
44 views

Ratio between positive and negative examples in a training problem

When training a 0/1 classifier, what should be the ratio of positive to negative, how to decide the ratio between them based on the classifier I use and the data set under analysis?
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1answer
62 views

Weighted least-squares negative fitted values

I am running a weighted least-squares regression (where all weights are strictly positive), where my dependent variable is a cross-section of variance values. Since variance is always positive (>=0), ...
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39 views

variable error on logistic regression/ proc catmod- Building predictive model

I am using logistic regression to fit a model with categorical/multinomial varaibles. data-description: There are over 300 variables as independent variables, sample size is 5000 which is divided into ...
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1answer
35 views

How to compare probabilistic classifiers?

Assume that we have a very long sequence (i.e. a list) of nominal-valued observations. For example: A A C B ... B A B C We have also a corresponding sequence of ...
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29 views

Are LASSO regression predicted values also biased?

Since LASSO regression biases coefficients to reduce variance, aren't the predicted values also biased? In my case I am looking at fitted values from a predictive logistic regression model with LASSO ...
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14 views

Discriminant analysis on SAS EM

Is the memory based reasoning same as discriminant analysis on SAS Enterprise miner? If not under which category of SEMMA can i find it Which methodology should i use for predicting. I currently have ...
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15 views

Kolmogorov Smirnoff and Predictive Analytics

I am trying to interpret the Kolmogorof Smirnoff test in the case of predicitve analytics to compare three models: a neural network, a decision tree and a logistic regression. The target variable is ...
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15 views

How do I best possible model future risks for Organization?

Using following concept is it possible to define or layout future risk or in security terms future root-causes that are critical for organization operations and businesses. Those concepts are:- 4 ...
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17 views

Confidence intervals for the Log Loss metric for model comparison?

Quite a few Kaggle competitions have used or are using the Logarithmic Loss metric as the quality measure of a submission. I'm wondering if there are other ways besides N-fold cross-validation to ...
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14 views

Different variable importance results with stabsel and mboost

I'm using glmboost in the mboost package to fit a boosted regression using linear models as the base learner. There are 13200 observations and about 75 variables, ...
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1answer
20 views

Risk Ratio (or Prevalence Ratio) in SAS Proc Glimmix

Is it possible to obtain risk ratio in proc glimmix. And if yes, how do I specify the base. E.g base 'male' in variable 'gender'. Below is a template of my model: ...
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41 views

Quantifying the predictive ability of a model developed from a huge data set? (variation of bootstrapping?)

I have a statistical model with around 20 predictor variables, built on 90% of a dataset consisting of over 600k observations. The original developer held out 10% of the original dataset for the ...
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16 views

Assessing predictor contribution to model output

Many of machine learning methods are considered as "black boxes". Examples of such methods are SVM, Neural Networks, Random forests etc. One may apply sensitivity analysis techniques (as described for ...
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2answers
55 views

Predictive with uniform likelihood

I'm trying to get a predictive density and currently getting something which I know can't be true (based on both logic and simulation based techniques. Here's the relevant information. $\theta$ is a ...
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0answers
20 views

Transformations in Simple Linear Regression [duplicate]

Suppose a linear model for Y in a single predictor var, X. If the residuals show a pattern of increasing variance (wrt X), sometimes a transformation of Y, Y'=f(Y) is considered (where f is sq rt, ...
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2answers
35 views

What is the main idea behind the power spectrum?

Assume that we have a time series and we have calculate the corresponding auto-covariance function. Having the auto-covariance function we can calculate the corresponding power spectrum and having the ...
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1answer
40 views

How to adjust data to remove influence of one or more features

For my first real data science project I would like to develop a model which better reflects review quality than "useful" votes. I am working with Yelp's latest Academic data set but this thinking ...
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29 views

Fitting a model to data for prediction - best choice for data

I have some data I need to fit a model to that can be used for prediction (interpolation). The data is summarized by the plot below. The black line is x=y. I want to be able to fit a model so as I ...
2
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1answer
51 views

c-index for parametric links in binary regression

I am conducting a binary regression using different sorts of parametric links (logistic, Pregibon, Aranda-Ordaz, ... see) and I would like to compare their predictive and classification perfomance in ...
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1answer
12 views

Predictive analysis based on history

Let me first say that I am a CS person and my knowledge about statistics is quite basic. I am trying to see what predictive analysis to use for a problem I am trying to solve. I will try to make my ...
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2answers
31 views

Predict Seasonal Variations

I am developing an application related to pharmaceutical industry. Certain items are sold in significantly higher quantities during specific periods of the year. For example, here in my country, ...
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6 views

given a set of pairs of graphs, build a model that accept a graph and predicts it matching graph

Training Data I have a set of pairs of normally distributed graphs, each with a concrete last sample (maximal X) Question I want to build a model (formula) from the data input: a single graph ...
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23 views

The way to evaluate the importance of an independent variable in a regression model

In a regression model, like y~( x1, x2, x3). Is there a test or a way to evaluate which independent variable, x1, x2, or x3, is ...
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34 views

Predictive model with combinations of dummy variables of different length

I would like to try to predict the amount of a public contract based on historic records where the main variables that I can fit against include: contact duration (continuous) number of buyers ...
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35 views

Reverse engineer a predictive model from a time series graph

I have found some real estate plots in a scientific article. These graphs mainly describe, the believes of the author of the development of the real estate market in the future for certain countries. ...
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23 views

Predictive Data Model - Is this approach correct?

I’m working on a data science project for a class where I’m trying to develop a model that predicts whether customers will lapse (definition below) in the future based on past data. I have a method ...
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33 views

Loss Functions and Evaluation Metrics

Do you have to evaluate with the same (or equivalent) loss function for model selection purpose? Say you have bunch of models to select. A loss function of one model in training stage is different ...
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26 views

How to improve linear model generalization when autocorrelation is present?

I have features $X_t$ and response $Y_t$ (all continuous variables) and my objective is to find the best estimate of $f(X_t)=Y_t$ where $f$ is linear, and 'best' is defined as lowest generalisation ...
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1answer
98 views

optimal binning in R

SPSS has an optimal binning function that helps categorizing into meaningful intervals continuous predictors when a binary response variable exists. I was looking for an equivalent function in R but ...
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1answer
31 views

build model with complicated types of feature variables

I have been asked to build a model to predict a life span of a material based on a couple of features. The features can be classed into the following categories: 1) The feature variables just have 0 ...
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1answer
37 views

Calculating probabilities using cox regression [duplicate]

I have done a multivariate Cox regression in R. The model fits to my data very well. Now, I would like to use my model and predict the survival probabilities of new observations. I am unclear how to ...
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1answer
64 views

Evaluating predicted vs observed - RMSE vs. Pearson's R interpretation

I'm evaluating the error in three cross-validated models plotting observations against predictions. To do so, I'm comparing the RMSE (root-mean-squared-error) and the Pearson's R between predictions ...
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46 views

Plotting the effect of a variable estimated by a regression model fit

Plotting the effect of a variable estimated by a regression model fit is quite interesting. However, I have some questions regarding this subject. Here is some example code: ...
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1answer
17 views

What are approaches to, accuracy and value of forecasting in/for highly volatile environments?

More details in my Quora question here: http://qr.ae/x4s5Z. Please note that this question is not about value, approaches and methods of forecasting in general, but specifically about forecasting ...
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7 views

How do I practically compute the Hessian of a linear-chain Conditional Random Field?

A linear-chain Conditional Random Field defines a pmf: $$ \begin{align} p(y|x) &= Z(x)^{-1} \prod_{t=1}^T \exp(\phi_t(y_t, y_{t-1}, x)^\intercal\theta) \\ &= \exp\left(\sum_{t=1}^T\phi_t(y_t, ...
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21 views

How to include the error term as an explanatory variable in a lm() model when the errors are auto-correlated?

I am wondering if there is a way to include the error term as an explanatory variable in a lm() model when the errors are auto-correlated. For example, I have a model that gives errors that has a ...
3
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1answer
69 views

Bootstrapping Hmisc::rcorrp.cens for paired concordance?

As Frank Harrell says here and other places, it's better to compare two predictive models (Cox proportional hazards in this case) wrt discrimination (C-index) using the paired U-statistic ...
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32 views

Machine Learning Models For Real Time Sales Data

I am working on a predictive analytics problem related to Sales where based on interaction with a prospect we try to predict whether the deal will close or not. In sales, the data updates whenever a ...
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1answer
43 views

Predict after using Box Cox Transformation

I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 ...