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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2answers
32 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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0answers
10 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
15 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
51 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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0answers
18 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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0answers
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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0answers
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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0answers
24 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
11 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
23 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
30 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
15 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 ...
2
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0answers
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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0answers
8 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
18 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
26 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
27 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
79 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
20 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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0answers
35 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
29 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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0answers
11 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
74 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
27 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
26 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 ...
1
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0answers
7 views

Different Effects of Detection Probability Between Presence and Count Data at the Same Location

I'm modeling habitat suitability for a large, mobile animal using occurrence data (presence only---I have no true absence data in this case) collected from camera traps (stations with automatic ...
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0answers
10 views

Modeling Arrivals With a Time Limit

I have some data (sample here): ...
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0answers
25 views

Intuition regarding regression through the origin

An exercise asked to obtain properties of the lineal model $$E[y_i]=\beta x_i\qquad i=1,\cdots,n$$ where $Var[y_i]=\sigma^2$. In one of its sections, we had to calculate and estimator for $\beta$ ...
9
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0answers
79 views

Can cross-validation be helpful if we are interested only in modeling, not in forecasting?

Can cross-validation be helpful if we are interested only in modeling (i.e. estimating parameters), not in forecasting? I see how cross-validation is extremely useful if your goal is to make good ...
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0answers
61 views

Multiple probabilities manipulation

Given a set of $n$ factors $F=\{f_1,f_2,\cdots,f_n\}$ where $f_i \in F$ is a probability between $[0,1]$ that is uniformly distributed, and they are independent events. Each of these probabilities has ...
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0answers
11 views

Visual examination of residuals for a large dataset

Often, as part of verifying that the assumptions of a model (such as OLS) are reasonable, I see advice to visually check that the model residuals are distributed relatively consistently around 0 as ...
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0answers
8 views

Modelling errors of linear & logistic regression

How can the errors of linear regression models be modelled to make the results even more accurate? Also, how are errors in logistic regression measured? Is it possible to model the errors of logistic ...
1
vote
1answer
24 views

GLM with empirical distribution

If I understand GLM correctly, to run a GLM model I need to specify the particular transformation $f$ that ensures the conditional distribution of $f(Y)$ given $X$ is from the exponential family. (I ...
0
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0answers
15 views

How to compare two time series data from two different models for a same variable?

I am working on the isotopic value of Evapotranspiration, an environmental variable. There are two models to compute this value, so I am planning to use these two models to compute the same variable. ...
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0answers
8 views

How to find weights that suit most in regression model

I have a data of football matches and I want to predict future outcomes. One thing I am sure of is that last few matches has stronger impact on outcome of next match, for what I might use weights ...
0
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0answers
37 views

Fitting heteroscedastic models using gls function

Consider the following heteroscedastic model: $$y_i = f(x_i, \beta) + g(x_i, \theta)\varepsilon_i, i = 1, \ldots, n, \tag{1}$$ where $f(\cdot, \beta)$ is the regression function and $g(\cdot, \theta)$ ...
0
votes
2answers
94 views

Which model is better? Multiple regression

Basically I am comparing between 3 models. I did some log transformation. I am trying to find which one is the best model. Model 1: ...
9
votes
3answers
134 views

Test of association for a normally-distributed DV by directional independent variables?

Is there an hypothesis test of whether a normally-distributed dependent variable is associated with a directionally-distributed variable? For example, if time of day is the explanatory variable (and ...
1
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0answers
31 views

PDF of sums, products of iid Normals

I've recently taken to looking at the distribution of a financial time series of the form $$X_t = X_{t-1}(1+W_t)$$ where $W_t$ is iid $N(0,\sigma^2)$. Expanding the equation out we get $$X_t = ...
3
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1answer
37 views

Testing for stationarity

We know that the definition of stationarity (either weak or strong) of a random time series involves having the same joint distribution or statistic (like mean or variance) for "any" set of time ...
0
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1answer
18 views

Is a normal distribution a good model of stock market returns across time? [closed]

I'd like to use something like numpy.random.normal to model random stock market returns for a given year, with mean of 7.25% and standard deviation of 19.8% (found using Excel), using numbers for the ...
0
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0answers
9 views

Proper Method to Add weights to variables

Given a dataset with different fields of criteria, all coded as 1 or 0, and also with a target which can take a value of 1 or 0, how can I create weights into the fields. In a banking example, let say ...
2
votes
0answers
29 views

Model time-series data for a Forecasting Model in R [closed]

I have time-series data (xts form) of power consumption at a 10 minutes rate and I do have temperature and humidity values as well. So my data looks like this: ...
1
vote
1answer
25 views

Estimating a “true” value with a noisy number of additive noises

I want to recover an estimate of the "true" value of a variable from a small set of noisy observations (5–20). I have an a priori model describing the physical process that generates the ...
1
vote
0answers
27 views

What statistical method should I use to model data?

I am trying to model a continuous numerical variable using a binary fingerprint (0 and 1)- I have used decision trees to model this, is this the best method to use? If not, what method would be best ...
0
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0answers
31 views

Reinforcement Learning & Text Mining

I was wondering if one could use Reinforcement Learning (as it is going to be more and more trendy with the DeepMind & AlphaGo's stuff) to parse and extract information from text. For example, ...
0
votes
1answer
47 views

Forecasting methodology and k-fold cross validation for a vector autoregression

This is a follow up question the question that can be found here, and is a result of me having implemented (after as careful evaluation as I'm capable of) the alterations and changes suggested. Below ...
0
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1answer
34 views

Do I have to add extra terms to a regression model?

Data: I have monthly temperature data for 90 years along with a climate index ('pdo') that influences temperature. Scientific question: is there a linear trend in temperature across time? I've ...