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Questions tagged [model]

A statistical model is a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically related.

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Machine Learning Model Evaluation

If a model is overfitted that means decent gap between training curve and testing/validation curve but achieves good precision and recall score,does that still indicating that the model is decent?
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AICc for small sample size

I would like to test the effects of salinity and temperature on parasite infection. Temperature and salinity are the fixed factors, the first one has two levels, the second one three levels. ...
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validating model on small binary dataset with balanced outcome

I have a model which is built on a dataset (N=288), and I want to validate it on another extremely small dataset (N=13) and it is only partly similar to the first one. As they really aren't similar I ...
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How would a drift diffusion model explain effects in reaction time but not accuracy?

How would a drift-diffusion model explain a case where a variable has an effect in reaction time but not in accuracy. I know that one explanation would be a speed-accuracy tradeoff, in which the ...
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Multilevel modeling in R and STATA

I am trying to transferring from STATA to R. I have tried to fit a simple model in R and Stata with an identical large dataset (obs>60,000). The model has three levels: obs->individual->community. ...
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Nonparametric approach for regression with a quadratic fit

I'm trying to figure out which nonparametric test I should run on my data. My data has residuals that are not normal, so I cannot run a linear regression unless I log transform it. However, log ...
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How can we plot the predictions of logistic regression model in order to see whether it is good?

I am working on a basic problem that requires developing a logistic regression model (the output is True/False, whether a person gets cancer). I have used glm() in R and got the model with some ...
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What is a good model for conditional data?

I am looking for statistical models of conditional data where some variables take certain values conditioned on values of other variables. For example, if $[x_1, x_2]$ are two variables defining a ...
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How powerful are second order interactions?

A lot of applications in statistics and machine learning model a phenomenon by second order interactions of variables and get good results. By second order interactions I mean, for a general variable $...
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What statistical model should be used?

What statistical model should be used when trying to look at a change of good ownership between a sample year set?
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Parameters, constraints and MLE of log-linear models

I want to use log-linear models to assess the type of (in)dependence between variables in $2\times2$ and $2\times2\times2$ contingency tables. In the process of doing so I would also like to ...
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How to choose the independent variables in a GLMM without performing stepwise selection? With a global model? How to decide then?

I am trying to conduct an inferential binomial GLMM with a large dataset and many independent variables. I was attempting to do a stepwise AIC selection but keep reading it is a bad idea. However, ...
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Bilevel optimization with multiple features

For my data, I have varying levels of a categorical variable, and for each level, I have a dataset of numerical values and positions of sensors for those values. Specifically, I have multiple objects ...
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Can I remove random effect from my model?

I have some data which I am getting a singular fit error and I would like to work out the reason so I can decide whether I can remove the random factor from the model. I have 835 data points with a ...
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Multivariate Generalized Least Squares

I'd like to use the generalized least squares (GLS) in the multivariate version. I have a response variable $\boldsymbol{Y}$ with dimension $n \times m$, where $n$ indicates the number of observations ...
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Level Variable in Change Model

I wonder if it is valid to add level variables in the change model. For example, if I want to investigate how air pollution affects population change, can I use the change of population from t-1 to t ...
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What model/significance test should I use to see if there's any significance between male and female sexual dimorphism on temperature over time?

In my lab I need to test if there's an significance between male and female sexual dimorphism on body temperature over 40 minutes. The temperature is simulated with a heat lamp and is turned on at 0 ...
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Model selection - three correlated binary variables

I wish to examine the effect of a vector of independent variables $X$ on three dependent variables $y_{1-3}$. The independent variables are identical for all three dependent variables, as are the ...
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Poor performance of model in test set [duplicate]

I am building a classifier neural net - my model is as give below ...
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1answer
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Model selection for GAM in R

Apologies in advance I new to this forum and to GAM models. I am trying to model complex ecological data. I have programmed a lot of GAM models using the mgcv ...
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Simple Keras LSTM model does not converge [duplicate]

I try to predict time-series with simple Keras LSTM model: ...
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1answer
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Batch differences in biochemical measurements - statistical solutions?

I'm working with inflammatory markers from human participants in a current study and have been stumped by between-batch differences. One third of the participants had their inflammatory markers ...
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Is there a branch of machine learning that can deal with near infinite state spaces

So I have a game type problem defined as follows; Up to 10 players Each player has: 64 tiles 200 piece types Up to 20 pieces in play at any time There's a random ...
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1answer
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Conditional expectation in Basic Linear Unobserved Effects Panel Data Model

I want to see that $ E[u_t|X_t,c] \Rightarrow E[X_t' u_t]$ in a Panel Data Models
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Is it a problem if in a regression one independent contains some values of another independent?

I am working on a panel dataset on public procurement. Each row represents a company bidding in a specific tender and submitting a bid. I am performing a regression analysis to predict when a company ...
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Cross Validation versus Ensemble Learning

After performing $k$-fold cross validation to find the optimal model, and or hyperparameter choices etc, it is common to re-train your (best) proposed model on the full training set, and quote this as ...
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Which model is correct?

I have experimental data frame. There are 4 experiments and 4 treatments within each experiment repeated 4 times within each experiment (balanced design). I would like to test the effect of experiment ...
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1answer
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How can you constrain values to be positive when fitting a model?

I'm currently fitting a model using maximum likelihood estimation on biological data (electroencephalography). Basically, I'm fitting normal distributions to several subsets of data (experimental ...
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Doing hyperparameter optimization, the smart way

Once our machine learning model is build, it takes quite a while to fine tune the hyper parameters to get good results. Though techniques like cross validation do exist, I wonder if there are some ...
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R: Difference between glm and gls

What are the differences between glm and gls in r? How do I choose which one to use for any set of data? I tried googling but nothing gives, are they the same thing in R? I know the other is general ...
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Model for analysis Lattice desing of genotypes in different enviroment (genotype x interaction interaction) in R

I want find a script of the model for make a ANNOVA in R Studio of lattice desing of 100 genotype (treatment) with two replication (10 plot per block) in 4 different enviroment. Lattice with ...
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1answer
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Model with “Integer Inflated” distribution Y

Forgive the poor statistical lexicon, I am a naturalist. I am trying to find the best link function to build a model to estimate a response variable (Y) that has what I called an "Integer Inflated" ...
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1answer
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Using a given polynomial formula in a lm() model in R

I am currently trying to fit a polynomial model to measurement data using the lm() function. fit_poly4 <- lm(y ~ poly(x, degree = 4, raw = T), weights = w) ...
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1answer
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Using a larger validation data set reduces the chances to select an overfitted model

Does a larger validation data set reduce the chances to select an overfitted model?
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1answer
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Model Deployment: export Scikit Learn Pipeline or Model only?

Following ML best practices, I use Scikit Pipelines to make sure my data preprocessing is the same at each model development iteration. Also as a best practice, once I have completed model ...
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How to test zero hypothesis with a linear model lm

I would like to test a zero hypothesis on a linear model in R ( H0: F = 0, H1: F/= 0). How do I do it? Is it adequate to just use a linear model and if its not significant, then H0 is true? My Code ...
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How to set up contrasts in a linear regression models that involve averaging over levels of another factor?

For simplicity, assume we have a linear model which looks like this: Outcome = beta0 + beta1*Treatment + beta2*Time + beta3*Treatment*Time + error where ...
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Fitting a model in R [closed]

How do I fit a model of the form $y = a + b \exp(−cx)$ to my data in R?
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1answer
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How do I run cross validation on a decision tree in an uplift model?

I have this model from the uplift package, ...
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How do I describe an interaction effect with categoric variables?

I have a continuous response variable. It is the conversion rate from an AB test. I noticed that the response variable changes meaningfully based on the experimental group, plus one other variable ...
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interpreting rank difference trend

I am doing some research utilizing popularity ranks in periodic data. I've been able to model the rank data using Zipf Law effectively. I am interested in understanding how the rank changes, as well. ...
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Stationary processes for AR, MA, ARMA

Depending on the parameters, the AR, MA and ARMA can be either stationary or non-stationary. For instance for an AR(1) process, if $|\phi|<1$, the process is stationary and else it is non-...
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Comparing a Z-score from a model fit on real data against a randomized input

I have a quite complex statistical model that fits the parameter $X$ for some input data. The form of this model is irrelevant for the further question (at least I think so). Let's say I provide two ...
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What is a 'true' model?

A short question, but I am somehow unable to find any concrete answer. I suppose it means that the model is as good as it can be? Containing all relevant variables and hence not suffering from any ...
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1answer
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How to solve an adaptive lasso model?

Assuming we are working with a linear regression model, lasso penalization solves: \begin{equation} \min_{\beta}\left\{\left\lVert y-X\beta\right\rVert_2^2+\lambda\sum_{j=1}^p \left\vert \beta_j\...
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2answers
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3D plot of Akaike Information Criterion (AIC) for suitable ranges of Lˆ and k

Giving that Akaike Information Criterion (AIC) is as follow: How can I Produce a 3D plot of AIC for suitable ranges of Lˆ and k. In other words what could be a suitable ranges of L to try? Moreover,...
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1answer
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Would a line graph be appropriate to display this specific data?

I want to display data that is of my own personal job applications/replies/offers between two different time periods. One where my name was a certain way, and one after I changed my name, but I want ...
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1answer
60 views

Intuition behind computing gradient for a model

I'm trying to understand gradient computation. The basic formula I found looks like so: ...
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1answer
23 views

What to do with very little training data

What are some popular solutions in dealing with very little training data? Do these solutions rely on generating more data (e.g. bootstrapping, SMOTE, etc.)? Or do they rely on methods that do not ...
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1answer
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Statistical Learning/Classification problem (True or false)

I think the answer is false, but I'm not entirely sure how to put it into words. The problem is as follows: An electronic store wants to build a model to predict the number of televisions that it ...