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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ACME Significant in Mediation analysis, but not Proportion Mediated and Fitting terminated with step failure warning
I am running a series of mediation analyses in R using the mediation package and the following code:
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Biased Sampling from a Non-Normal Dataset
For my analysis, I'm interested in a particular subset from a non-normally distributed population. I would therefore like to generate a sample from that population. The sample will have drastically ...
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Estimation of bivariate function with one variable being constricted
Suppose the following classical supervised regression setting,
$$y_{i} = f(x_{i}) + \epsilon_{i}, \quad i=1,\cdots,n,$$
where $\epsilon_{i}$ are i.i.d. zero mean Gaussian noise.
The above regression ...
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U -statistics for bi variate sample problem
Let $(X_1, Y_1), (X_2, Y_2),....,(X_n, Y_n)$ be iid random variables with joint distribution function $F(x, y)$ and $F(x), G(x)$ be the marginal distribution functions of $X_1$ and $Y_1$ respectively. ...
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Can I aggregate several continuous variables into percentages and then compare those percentages between groups?
I have a dataset with the concentrations of several lipids. I'm interested in finding lipids that are altered between two conditions, but the lipids are not indepentent from each other and the ...
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How to compare peak location and tail length of two different distributions?
I have the distributions of the fraction of people in each income bracket in a town in 1990 and 2020. The total sample size is the same in both, and assume that the incomes have been adjusted to ...
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Asymptotic distribution of $U$-statistics
Let $(X_1, Y_1), ...., (X_n, Y_n)$ be iid random vectors with marginal distributions functions $F(x)$ and $G(x)$ (both are continuous distributions) respectively such that $F(0)=G(0)=\frac{1}{2}$. ...
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Can I use Mann-Whitney U test for within group analysis?
I am conducting a within-group study where participants rate the perceived helpfulness of ideas on a Likert scale (DV) across two different days (Day 1 and Day 2), serving as the independent variable (...
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Statistical test for small non-parametric dataset with more than 2 dependent groups
I’m trying to figure out the most appropriate test to use for a small water quality dataset (n = 10 sampling visits at 6 river sites, upstream to downstream) with the following characteristics:
-not ...
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How to determine goodness-of-fit between non-parametric 2d-datasets
Lets say I have a set of paired x' and y' values and I have a N sets of reference values also consisting of paired x and y values. I would like to determine which reference set best matches by x'y' ...
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Estimate the likelihood of two continuous samples of unknown distribution
Consider two continuous and unknown distributions
$$X : {x_1, x_2, ..., x_n}$$
and
$$Y : {y_1, y_2, ..., y_n}$$
both can be tagged as time series with $n > 8000$.
I need to estimate the likelihood ...
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About regression analysis with categorical variables
Suppose my dependent variable is a continuous variable and is normally distributed. And I have three IVs: one is a continuous variable, and the other two independent variables are categorical. What ...
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Identifying the type of missing data and the post hoc test that can be carried out for Skillings Mack test
I have a non-normal paired sample dataset. Each row represents a dog that has been tested for an experiment. Each dog was provided with three cues (treatments): 5s cue (aka only face cue), vocalone (...
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Implementing Convolution Function for Gaussian Kernel in Python for PDF Estimation
I am currently working on estimating a probability density function (PDF) nonparametrically using a Gaussian kernel. My goal is to determine the optimal bandwidth $h$ that minimizes the cross-...
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Deriving Sample version of Anderson Darling test statistic from the theoretical version
In literature, I have seen two types of Anderson-Darling test statistic. One is expressed as
$A_T^2 = n\int_{-\infty}^{\infty}\frac{(F_n(x)-F(x))^2}{F(x)(1-F(x))}dF(x)$ and the other is given by $A_s^...
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