Questions tagged [nonparametric]
Use this tag to ask about the nature of nonparametric or parametric methods, or the difference between the two. Nonparametric methods generally rely on few assumptions about the underlying distributions, whereas parametric methods make assumptions that allow data to be described by a small number of parameters.
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Are these two estimated regression coefficient asymptotically equivalent? If not, which one is more efficient?
Suppose I have $Y=\beta_1X_1+\beta_2X_1X_2+g(X_2)+u$, where $E(u|X_1,X_2)=0$ and $S=g(X_2)+e$ with $E(e|X_2)=0$. I have a random sample $\{Y_i,X_{1i},X_{2i},S_i\}_{i=1}^n$. Suppose I first use a ...
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Kruskal-Wallis statistical power
Given that the Kruskal-Wallis $H$-test has been computed, the epsilon-squared estimate of effect size can be calculated as
$$E_R^2=\frac{H}{(n^2 - 1)/(n+1)},$$
the number of the compared groups, the ...
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Give the distribution of an asymptotically Normal statistic, conditional on a function of sample ranks, and describe regression parameters
I'd like to write the following, about conditioning a normal r.v. on a rank statistic, but I'm unable to recall a specific theorem or theorems I can cite:
"Let $\hat{\rho}$ be an asymptotically ...
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Data is not normal, want to test for interaction too Any non-parametric test? [duplicate]
I have a sample size of $310$ individuals that comprises samples collected along the elevation gradient and at different times. I want to see how the variables differ along the elevation, and with ...
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Model fitting with Chinese Restaurant Process
I am trying cluster a trajectory, consisting of (state, action) sequences, by assigning them to the most likely model that generated them using Chinese Restaurant Process. Basically my goal is to ...
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Friedman test for small sample single arm trial?
I am looking at a single arm clinical study with a small sample size (n=14), where each patient was assessed 5 times (weeks 1-5, once a week). I have constructed linear models to see if the change-...
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Basic question regarding before and after intervention analysis on groups of different sample size
Apologies for the basic nature of this question. I have collected data before and after an intervention and assessed response to the intervention using a survey. This was a basic survey using a 1-5 ...
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References: convergence rates of kernel regression, exchangeable data
I have been studying Kernel estimation; in particular, the Nadaraya-Watson estimator. I am interested in studying the rate of convergence in L^p of the NW (or similar) estimators for subgaussian ...
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Comparability of means for non-normal distribution and small sample size
I have to perform a comparability study between pre and post change of a production process.
I'm using the final purity to measure comparability and wanted to compare medians between pre (n=40 runs) ...
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Confidence interval for sum of product of scaled binomial random variables
I have discrete, independent, but not necessarily identically distributed random variables $X_1,\dots,X_n$ that take on non-negative integer values. Each random variable has unknown distribution ...
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What do these Wilcoxon values tell me?
I did a pre and post-test with a small number of students ($n=12$).These values came after a science intervention that took $4$ weeks. I used a Wilcoxon test for 2 related samples as there was not a ...
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Comparing the output distribution of two ML models
Consider a regression task (e.g. predicting house prices) with a given train and test sets.
We start with constructing a linear regression model, in which we assume $y_i=X^T\beta+\epsilon$ with $E[\...
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BART with non-parametric heteroscedastic noise?
Is there a variant of BART that robustly captures noise that is both heteroscedastic and non-parametric (or has an a-priori unknown parametric form)?
For example, a BART that could fit this test data:
...
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Estimate multivariate distribution with several variables on real data (continuous and categoricals) and sample from it
I have a complex dataset, collected through a survey, with both continuous (such as Age, Body mass index, etc..) and categorical variables (i.e. Gender, Education, etc..). I want to estimate their ...
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Choice of test for Comparing Chatbot vs. Group of Humans on Ordinal Scale
Background
I want to compare chatbot (e.g. ChatGPT) performance on medical questions with a group of human doctors. Each question will be answered and scored on an ordinal scale 1, 2, 3, 4 or 5. I ...
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non-parametric-ANOVA
I have a dataset containing angles. They represent the bending angle that a seedling makes to go toward light. Genotype A is WT and A is the one we are testing. We removed a PKS gene, wich is ...
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SIgnificant ANOVA but not significant post hoc ... what can I do?
I am analyzing some IHC data on the density of cells in two brain regions(factor 1) in two closely related species(factor 2). My data is composed of an n of 6 for each species and is not normally ...
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Statistical Power Analysis for reight skewed data sets
I have price Data for a set of items for various time steps as i collect this data regularly. The data is very large (over 250000 items usualy per group per point in time) and not normaly dstributed ...
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What is this nonparametric goodness-of-fit test?
I wrote down a goodness-of-fit test that I have not seen before.
However, it is quite elementary and has many applications, so I
bet it must have been known. Could someone tell me its name?
Setup. The ...
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What non-parametric test for multivariate binary data should I use?
I have two different groups of participants ("g" and "b") answering the same set of questions. Group "g" answered questions in the same order. Group "b" ...
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Statistical test for Likert scale perceptions of application of a HR development model to dental restorative specialty training
I'd be most grateful for some advice about suitable statistical testing for a research project.
It's a survey questionnaire to restorative specialty trainees and alumni trainees exploring the ...
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How to estimate how heavy a tail is?
Suppose I have data coming from a single variate distribution. I want to estimate how heavy the tail of the distribution is. For example, if the data comes from the Zipf distribution, I would want the ...
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Very small degrees of freedom when using yuen function in WRS2 package of R
I'm trying to run a number of independent samples t-tests. I'm using the yuen function in the WRS2 package because my data are non-normal and ordinal. My sample ...
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Parametric or non parametric mixed effect models for very small sample size in ecological/soil data?
I am running an ecological/environmental experiment in a salt marsh with 5 experimental treatments and 3 replicate soil samples taken from each treatment. I have been taking measurements from these ...
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Bayesian analysis of non-normally distributed variable
I would like to use an Bayesian approach to compare a continuous non-normally distributed variable taking values between -1 to 1 between two populations. The measurements are not paired.
Overall my ...
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learning guarantees for gaussian weighting of training points
I have my training data for binary classification that consists of $N$ pairs $$(x_i\in R^F, y_i \in {-1, 1})$$ $i\in [1,\dots,N]$. My classification rule of a new point $x$ is simply
$$
\hat{y}(x) = \...
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Boostrapping from Exponential Sample to estimate the quantiles
I have the problem where I want to estimate the quantiles of a distribution by bootstrapping. Fortunately I do know the original DGP which I chose to be an Exp(1/4) distribution and so the theoretical ...
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How do I use bootstrapping to compare more than two means?
I'm trying to use non-parametric bootstrapping to compare mean values in a 2 (between) by 4 (within) experimental design. I cannot use mixed ANOVA because the sample values are not normally ...
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Complete Statistic for a family with finite r-th moment
Consider the family of all continuous distributions with finite $r$-th moment (where $r \geq 1$ is a given integer). We denote this family as, $$\mathscr{P}_r=\left\{f:f \ \text{is a pdf and} \int|x|^...
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The hunt for a 'nice' flexible distribution [duplicate]
Background
Suppose I have data $\mathcal{D}_1, \cdots, \mathcal{D}_n$ with each $\mathcal{D}_i$ containing $m$ observations $X_{i1}, \cdots, X_{im}$; these observations are of unknown distribution, ...
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