A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more random variables. The model is statistical as the variables are not deterministically but ...

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4 views

How to define holt winters model for forecasting using output R

My research is comparing between SARIMA & HoltWinters multiplicative. I got confused when I tried to define forecasting model for HoltWinters multiplicative, because I thought model SARIMA and ...
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1answer
16 views

Is the likelihood statistic applicable for model selection in machine learning?

Minimising the likelihood ratio statistic is often used as a criterion for model selection in connection with linear and related models and statistics such as as AIC are an extension of this practice, ...
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1answer
8 views

Time Series Modelling[Issue with modelling the residuals]

I am doing the sales forecast. I found the trend and seasonality manually for my time series data. Regressed time series data against the trend and seasonality and found the residuals. The residuals ...
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6 views

Identifying sequential patterns and deciding which ones are useful

So, basically I have a problem in which I have, over time, the appearance of different features, each feature containing different categories (where categories belonging to the same feature cannot ...
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13 views

Selection Bias and Controlling Covariates

I am currently performing a retrospective study that is comparing a surgical procedure vs a modified version of the same procedure. There is obvious selection bias because of the selection criteria ...
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0answers
22 views

Why Does Adding Variables to a Model Make it Non-Significant?

I have what I'm assuming to be a basic stats question. I am comparing the survival functions of three study groups (i.e., the "main" IV under investigation) using ...
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1answer
50 views

Predictive model developement for logistic regression?

In the statistical courses I've taken, which are mostly introductory, when I have a model I would make hypotesis tests to reduce it to the simplest form and am effectively done. It is my understanding ...
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8 views

Time-varying predictive model for a set of proportions

Suppose there is a casino where people bet on a weekly horse race. On Sunday, the casino publishes the prices for a wager on each horse for the upcoming Saturday's race. Everyone who wagers on the ...
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19 views

Using ABC package from R cran with a C++ simulator [on hold]

I developed a simulator in c++ and I would like to use the R Cran package "ABC" from Csillery et al with that simulator. There seem to be many ways to make the two programs interact, but what would ...
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1answer
51 views

Multi-Channel Attribution Models: How to Measure Accuracy?

What methods are there, if any, that measure or approximate the accuracy of attribution models? I'm looking for something purely based in (real) data; preferably something analogous to typical cross ...
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25 views

My regression shows zero

After running my logit model I had one of the variables showing zero.. When I run the mix I get a zero and a dash when I run the odds ratio. It looks odd to me cos I haven't come across any such ...
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0answers
12 views

How to retrain a production classifier that blocks its own positive examples?

I'm looking for help understanding how to re-train a fraud detection classifier that's been deployed to production (where it successfully blocked much, but not all fraud coming into the system). I ...
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17 views

Notation of an integral in DSGE model [closed]

I am new to consumption being denoted by an interval as opposed to a summation. In Gali's (2008) book, he denotes consumption as: $C_t=\left(\int_{0}^1 C_t(i)^{1-\frac 1\varepsilon} \, ...
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20 views

ARIMA model over- or underfitting: compare training and validation performance

I'm doing research using seasonal and nonseasonal ARIMA models. Here's the result of model identification: Based on many sources, Your model is overfitting your training data when you see that ...
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1answer
31 views

model overfitting vs applicability

Consider two models I built: Model A I use a Neural Network to build a classification model and get a model that over fits , lets say the FPR in test set in 2 times that in train set. I am ...
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11 views

Which Algorithm and the Steps to be used for a “Product Brand Classification Problem”

I have a product dataset which contains just 3 fields, product_title, brand_id and category_id (in order). The problem is to identify the brand_id, using the other features (product_title and ...
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18 views

Can we use cross validation and bootstrapping together?

I would like to estimate the model parameters from n data samples in a training data set. I want to know if I can use bootstrap and cross validation jointly. For instance, I have n data samples. ...
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0answers
20 views

Statistical modeling of biomedical data in industry and hospitals: scopes and ways to do so

I hope my questions still follows the guidelines of this community. It's about my job-search. I'd like to know whether and how statistical modeling of biomedical data are in industrial or hospital ...
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0answers
21 views

Linear growth model estimation using dlmModPoly in R

I am dealing with Dynamic Linear Models (DLM) and I am trying to build a linear growth model in r using the function dlmModPoly ...
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1answer
33 views

Why L1 Regularization does not work with Calculus Training methods?

I quite understand What is L1 and L2 regularization, but the authors of articles keep saying that: To summarize, L1 regularization sometimes has a nice side effect of pruning out unneeded features ...
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50 views

VAR model selection, auto-correlation specification issues

I am encountering the following problems and I don't really know which model a should pick. All model selection criteria indicate that I should take the model with 1 lag. After building the ...
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21 views

Survival analysis to model waiting times for buses

I need some way to model what is the expected waiting times when someone can take one of a set of buses, with different time/frequency characteristics. Bus 1 - There are 10 buses an hour, but ...
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0answers
22 views

Model for count data with different exposure time (some times are terminated by death)

I am looking for most suitable model for count data in the following case: we collect number of patient's visits in a hospital for $t_i$ days ($t_i$ varies across subjects) some patients died ...
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2answers
74 views

How to choose between ROC AUC and F1 score?

I recently completed a Kaggle competition in which roc auc score was used as per competition requirement. Before this project, I normally used f1 score as the metric to measure model performance. ...
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28 views

How to think about the architecture of the Convolutional Neural Network?

Recently, I've started to learn more about CNNs to use them in some computer vision tasks. At the moment, I have roughly good knowledge about different parts of a CNN such as layers, solvers, loss ...
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1answer
19 views

model to predict variable evolution

Suppose that I have a set of variables X1 X2 and X3 that explain the evolution of a ...
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1answer
56 views

Discretizing Continuous Outcomes: good examples?

My continuous dependent variable has a lot of error in it. Hence, I was thinking of discretizing it, to reduce the error for my modeling effort. But firstly, the main focus of my modeling effort are ...
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19 views

Modeling error in regression

A few weeks ago I posted in this forum about a regression analysis I wanted to run. My outcome was number of organs and they values went from 1-7. Well, as someone pointed out, I could have some bias. ...
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13 views

How to model this variable?

I'm doing some data prep on a dataset provided by a telecommunication company. There is a continuous variable that indicates how many months have passed since a customer renewed her contract. However, ...
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5 views

Control the composition of scored data based on certain variables

So imagine you had some sample with the following demographic breakdown: Gender: 30% Female 70% Male HHI: 30% Less than 50K/year 50% 50K - 100K 20% More than 100K and you want to build a ...
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25 views

How to build a model from data with a proper hypothesis

I have a large dataset of items in a store and how they sell. It looks somewhat like this: ...
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0answers
15 views

Categorical mixture model in PyMC2

I am currently trying to implement a simple categorical mixture model in PyMC2. However, I am not able to get it to run after trying some possible solutions. Here is my current attempt: ...
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1answer
24 views

Stationarity Testing on different time series data

For linear regression modeling, I have macroeconomic data that goes from 1985-2016 which i will use as my independent variable. My dependent variable data ranges from 2002-2016. My question is for ...
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1answer
32 views

What is a good general guideline for mixed effects model building?

Suppose I have a dependent variable and half a dozen possible predictors. This experiment is wholly exploratory. What would be the best approach to discover which predictors (and interactions between ...
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0answers
16 views

Model panel data with ONLY time-invariant variables

I have a large dataset containing computers and their specifications. I collected 20 different prices over a period of 20 weeks for each computer. Now I want to build a model with the price as a ...
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24 views

White test confirms heteroskedasticity while Breusch-Pagan test doesn't [duplicate]

I'm using SAS in order to create a model for a cars datasets. The response variable y, is the price of the car. By the way I'm using the PROC MODEL statement in order to check heteroskedasticity. This ...
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9 views

Situation based predictive and explanatory models

So the question I am about to ask can be very subjective however I will try my best to ask it in a way that will generalise based on different situations or datasets. I am not comparing predictive ...
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0answers
19 views

Which model(s) are appropriate for this kind of data

So, I tried to implement a model on some data. The dependent variable is a ratio that can get higher than 1, is lower bounded by zero and, seeing figure 1, is left skewed.Thus, a logit regression is ...
1
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1answer
30 views

Normalising features extracted using a CNN?

I have used a pre-trained CNN to extract features from training and test images sets. The same CNN was used for all images. The CNN includes normalization layers. Before training a classifier (SVM ...
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1answer
29 views

Too many bagging estimators?

I am bagging 20 SVMs using the full training set. I have found the best SVM params using grid search. The validation performance is quite good, but performance on the training set is disappointing. ...
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0answers
13 views

Count data regression model formulation

I am working on one of the discrete probability distribution having pmf as P(x)={p^log(1+x^c)}-{p^log(1+(x+1)^c)} 0<p<1; c>0; x=0,1,2,. It fits well ...
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0answers
82 views

Principal components analysis: relationship between first and second principal component

I'm really struggling with understanding the idea of Principal Component Analysis and would appreciate any help. We have a m multivariate input time series $ \begin{align} X_{t} &= ...
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1answer
22 views

Interpolation vs nonlinear Regression [duplicate]

I was playing with the concept of Interpolation in Python and ended up with this plot: ...
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1answer
21 views

Given a biological dataset with measurements over a year, how can I identify seasonal variation, if any?

I have a biological dataset and I am interested in answering the following question: Are the measurements dependent on time-of-year/season? I use R for my analyses
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38 views

Dependent variable is count data, which method to use?

Which method should I use to analyse the relationship between count variable (absent days) and other 4 variables? Should I standardise Size variable? Please recommend some further literature/ ...
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1answer
32 views

Discrete or continuous variable

I am trying to model Ip adress to cretae a fraud detection framework. So I am wondering if Ip Adress is a continuous or discrete or categorical variable. Bests
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12 views

Find the underlying model of data using different predictor variables

I have energy consumption data for a duration of one-month. The frequency of data is half-hourly. The features of dataset are temperature - temperature value at particular time instant humidity - ...
3
votes
1answer
75 views

Is there a method to plot the output of a random forest in R?

Nice and simple. I've spent two hours googling, reading cross validated, and several r blogs to attempt to find a simple method of outputting the representative tree in R. I was attempting to ...
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1answer
33 views

How to interpret plots of point pattern models

I am struggling with the package spatstat and would really appreciate some help. I do not know how to interpret the "Fitted trend" and "Estimated SE" plots that one can get with the following code: ...
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0answers
27 views

Large sample with many little groups of dependent observations

I work with traffic crash data and my sample consists of about 165,000 injured people distributed over roughly 107,000 crashes. The prevalent approach in traffic crash analysis is to look at every ...