# Tagged Questions

Analyses are underdetermined when the number of parameters to be estimated is greater than the number of data. This problem is also referred to as 'p >> n'.

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### How to identify a SEM with formative dependent variable (with R's lavaan package)?

I have a formative construct in a structural equation model (SEM) which I would like to estimate with the function sem in the ...
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### Do I have too many variables and not enough data points for cluster analysis?

I have 75 observations and 152 variables. I want to perform cluster analysis. If I perform cluster analysis and this data will the results be meaningful? Do I need to reduce the number of variables ...
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### Detecting significant predictors out of many independent variables

In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
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### Solving a practical machine learning problem

I am currently doing my Phd in computational biology at Stanford. I get the data I need to answer the questions I am interested in. The data sets are sometimes "large" and these large problems take ...
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### LASSO prediction model question

I am trying to create a prediction model with 33 predictors (brain metabolite levels in various regions) and 8 observations (cognitive test scores) with p>>n problem using LASSO in MATLAB (...
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### Dataset for Least Angle Regression

I have read that least angle regression is good for high dimensional data. I didn't actually understand the meaning of high dimensional data, so does this mean $p>>n$ case? And does anyone know ...
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### Applying ridge regression for an underdetermined system of equations?

When $y = X\beta + e$, the least squares problem which imposes a spherical restriction $\delta$ on the value of $\beta$ can be written as \begin{array} &\operatorname{min}\ \| y - ...
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### Interpretation of regression coefficients obtained from applying left inverse of regressor matrix in an underdetermined system?

If $X^\dagger$ is the pseudo-inverse of $X$, $\beta = X^\dagger y$ is the least squares solution for $\beta$ when $y=X\beta$. In the overdetermined case, applying $X^{\dagger,L} = (X^TX)^{-1}X^T$ ...
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### Dealing with underdetermination in Bayesian models

Bayesian models are supposedly well equipped to deal with high-dimensionality problems, and can handle sparse data well, too. But suppose I've created a model that estimate more parameters than there ...
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### Feature selection & model with glmnet on Methylation data (p>>N)

I would like to use GLM and Elastic Net to select those relevant features + build a linear regression model (i.e., both prediction and understanding, so it would be better to be left with relatively ...
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### How to test for differences between groups over time when N is less than the number of levels

I have done a live cell imaging time course over 24 hours, and have a result for each hour. I have 3 experimental groups and 1 control group. What I want to know is if any of the experimental groups ...