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

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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19 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
39 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
11 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
30 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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4 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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19 views

Incorporating outlier points into a unified prediction model [closed]

There is a data set including multiple outlier points. If we want to build a regression model based on these points, what are the general approach to handle these outlier points? Are there any good ...
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0answers
18 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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29 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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33 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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17 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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18 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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0answers
21 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
55 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 ...
0
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1answer
30 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 ...
0
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1answer
27 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 ...
0
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1answer
24 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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32 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: ...
0
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1answer
15 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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6 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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0answers
19 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
53 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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8 views

How to not only compute predicted values but also standard errors [migrated]

I am trying to generate standard errors for predicted values. I do manage to generate the predicted values, however, do not get the standard errors. Instead, I consistently get the following error ...
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0answers
29 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
20 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 ...
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0answers
20 views

How can I make decisions from the output of predict.coxph()?

I'm studying Survival Analysis for the purposes of customer churn modeling. I've managed to put together a model, but I'm confused about what to do with the output. As I understand it, the output is a ...
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17 views

confidence interval for aggregated expectation from logistic regression

My model steps: 1.I fitted a logistic regression model $Y\sim X$; 2.then get the probability $P(Y=1)$ for each record; 3.then I summed the probability $R = \sum_i(P_i(Y=1))$ to be the expected return, ...
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2answers
31 views

Can a 1st Order Model capture information that a 2nd Order model can't?

Let's say we have some sequence $y_1 \ldots y_n$. By definition, a 2nd order Markov model can capture more information from the sequence than a first or zeroth order Markov model. What I'm interested ...
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9 views

analysis strategy for selecting and/or transforming correlated continuous biomarkers to predict binary endpoint

I am given a simulation task to come up with several analysis strategies and compare their relative performances. The horizon is wide open; I appreciate all recommendations of methods and references. ...
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1answer
22 views

Why is the posterior probability for the total number of carts being 60 about 0.006?

I’m reading Think Bayes by Downey and exploring the famous locomotive problem. After seeing cart 60 of a train zoom by, I’m trying to determine the posterior probability of there being X carts in the ...
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3answers
58 views

Good applied text for linear regression

I have a pure math background and am now studying statistics. For additional study in my linear regression and time series class, my professor suggested a more applied text rather than a higher level ...
0
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1answer
18 views

R- Is there a package for modeling Evolving Behavior of eCommerce vistors from click-stream data?

I am referring to the model outlined in an oft-cited paper by Fader and Moe : (https://marketing.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=3817) I am trying to predict whether ...
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32 views

How to interpret residual vs fitted values plot with clustered points

I am performing a multiple linear regression and I have a plot of the my first two explanatory variables vs the residuals and also a plot with the residuals vs the fitted values. I am not quite sure ...
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5 views

Dependent predictors with converse effects on the target

I am trying to create a predictive model for marketing in the natural gas field. The model is supposed to guess how probable it is to make a contract in that particular building given many internal ...
0
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1answer
116 views

Suggestions needed about classifier fusion

I'm working on a classification problem which involves two classifier to observe a single event. I'm providing a high level description of the problem without going into the technical details (the ...
0
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1answer
24 views

Modeling remaining duration for prediction

Suppose we're in the business of repairing broken specialty widgets and reselling them. At each point in time, we want to predict how much cash we'll make in the next 30 days on the existing ...
0
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1answer
59 views

Linear Regression In R and test of constant variance

I am trying to construct a regression model in R.I am getting an error while predicting the model. I am not sure if the newdata(which is my validation set) should be a data frame? ...
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21 views

How to develop a robust procedure to select a predictive model

Imagine you have a matrix, M, of n input variables and m values per variable. There's also a ...
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2answers
77 views

Predictive posterior distribution with multivariate normal distribution

Suppose I have a multivariate normal ${\bf{Y}}|{\bf{\theta}} \sim {\bf{MVN}}(X {\bf{\beta}}, \sigma^{2}H(\phi))$ where ${\bf{Y}}$ is a set of observations ${\bf{Y}} = ...
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0answers
9 views

glmer predictions: how to extract scores that contributed

I've fit a logistic regression mixed effects model with glmer in R and I'm doing predictions with it. Given a new data that needs a probability prediction, I am interested in extracting the fixed and ...
2
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2answers
103 views

Importance of multiple linear regression assumptions when building predictive regression models

As far as I know, one can differentiate between two main goals of the regression analysis: The goal is understanding causal relations between variables. Here, one has to check several common ...
4
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2answers
180 views

Should train and test datasets have similar variance?

If variance of test dataset is lower than the one of the train dataset is it worth splitting the data? Since we know our dataset will always be limited is it fair to select models under the above ...
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0answers
20 views

Hard Case - prediction of chain stores revenue

Data about average monthly revenue from 2000 stores around whole country. Gini coeff. of reve around 20%, with 50% of observation around average, very thin tails of distribution Explanatory ...
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25 views

Initiator follower analysis with time series data sets

I am a newbie to this forum. I searched different white papers and codes on google but couldn't find a solution, that's when I registered on this forum.. Please share in case you guys have a idea as ...
0
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1answer
17 views

Multiple regression with dependent variables

I have a dataset with 3 variables (X,Y and Z) and I want to find the best estimates for the constants a,b,c & d. I have been looking into multiregression analysis, but that does not seem to work ...
0
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0answers
34 views

How to retrieve the prediction equation in R?

I have developed a prediction model prototype in R. The model uses Support Vector Regression to predict. But I need to develop the entire solution in Visual C++ for a real life implementation. I ...
1
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1answer
40 views

Residual Value Prediction For Used Electronic Products

I am trying to predict the long term residual value of a product with only the releasing price. I have collected some data off the Internet related with one phone type, and it is pretty obvious that ...
0
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2answers
52 views

What other predictive models should I consider

I have a numeric dataset involving 34 input variables and one output variable. I'd like to fit a model to the data so to be able to make predictions (regression problem). So far I've tested ...
5
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1answer
71 views

Product price prediction - include important external factors

I need some hint over what is the general prediction solution to modelling products prices in such a case: I have several models (types) of the product I want to predict prices for each of these ...
4
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4answers
184 views

Predicting time to finish

Out of curiosity, I want to understand how to model this problem. I've been hearing people suggest the use of linear regression but I am not sure how to encode this problem (included my attempt below) ...