Questions tagged [optimal-scaling]
Optimal scaling or optimal quantification is an algorithmic approach to transform categorical variables into scale (interval) ones which would be "optimal" in some statistical sense (for example, their linear correlations will be maximized). There exist nonlinear "optimal scaling versions" of many classic linear kinds of analysis, including regression, PCA, etc.
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scaling for SVM destroys my results [duplicate]
I'm applying standard 0-1 scaling of features before SVM classification for financial data but the results are worse. This is the results before scaling
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What is the advantage of transforming variables from nominal to ordinal/numerical when it reduces variance explained in CatPCA?
Context
I have a dataset of 8 categorical variables. And I want to apply Categorical Principal Component Analysis (CatPCA).
Before doing that, I have been advised to look at the transformation plots ...
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How can I use optimal scaling to scale an ordinal categorical variable?
In an answer to this question about treating categorical data as continuous, optimal scaling was mentioned. How does this method work and how is it applied?
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How can we verify the intuition that in the RW-Metropolis-Hastings algorithm with Gaussian proposal too small and too large variances are bad choices
Let $d\in\mathbb N$ and consider the Random Walk Metropolis-Hastings algorithm with a Gaussian proposal kernel $Q$ such that $Q(x,\;\cdot\;)=\mathcal N_d(x,\sigma^2_dI_d)$ for all $x\in\mathbb R^d$.
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How does the celebrated result about the diffusion limit of the Random Walk Metroplis-Hastings algorithm help us to find the optimal scaling
Let
$d\in\mathbb N$ with $d>1$
$\ell>0$
$\sigma_d^2:=\frac{\ell^2}{d-1}$
$f\in C^2(\mathbb R)$ be positive with $$\int f(x)\:{\rm d}x=1$$ and $g:=\ln f$
$Q_d$ be a Markov kernel on $(\mathbb R^...
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Scaling regression coefficients Take 2: Gelman (2008) approach
I am asking a follow-up question about interpreting regression coefficients that have been scaled following Gelman's (2008, 2009) recommendations.
Original recommendation to divide continuous ...
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Estimate the asymptotic efficiency of a Markov chain sampling by the method of batching
In the paper Efficient Metropolis Jumping Rules, the author is writing that he used "the method of batching" for the estimation of $\operatorname{eff}_{\overline\theta_i}$ in Table 1 (on page 605). ...
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How to understand optimal Scaling in R: The Package homals for novices
Does anyone know of a step-by-step guide for the practical implementation of Gifi Methods for Optimal Scaling in R: The Package homals? Although I have an OK theoretical understanding (thanks chl for ...
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Optimal scaling level for variables categorized from continuous data
A variable was categorized into $10$ equally spaced intervals from a continuous variable which was originally in proportion. Now, we have to use this variable in ...
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K-means to cluster texts, scaling
I want to cluster a folder of texts. I created a data file where for each text, I write whether a certain word appears in it or not. I want to cluster according to this.
So my matrix is globally only ...
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categorical predictors in partial least squares
I am interested in running a partial least squares analysis using PROC PLS in SAS 9.4. I understand that, by default, the predictors and response variables in PLS are centered to a mean 0 and scaled ...
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