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1.238 nonparametric questions.

I've got some data (158 cases) which was derived from a Likert scale answer to 21 questionnaire items. I really want/need to perform a regression analysis to see which items on the questionnaire ...

I have samples $a$ and $b$ from pre and post treatment conditions on same participants. The empirical distributions of both these samples are skewed positively. The paired differences $a_i-b_i$ do not ...

Consider the following scenario: There exist $N=10$ participants in a study. Each participant $i$ is monitored under baseline conditions and later under experiment conditions. That is, the baseline ...

I've been trying to calculate and graph the correspondent power for the wilcoxon signed test and haven't had any luck. I tried simulating two normal distributed samples, apply the wilcoxon test and ...

I wish to run a Levene and Brown-Forsythe test to determine whether its safe to assume that different groups have the same variance of a given variable (say variance of wages across different regions ...

I need to run an inferential test on some large data where the individual data points have a heavily skewed distribution. I'm considering doing a paired t-test across a number of days comparing the ...

Lets say I have two or more sample populations of n-dimensional continuous-valued vectors. Is there a nonparametric way to test if these samples are from the same distribution? If so, is there a ...

What are state-of-the-art alternatives to Gaussian Processes (GP) for nonparametric nonlinear regression with prediction uncertainty, when the size of the training set starts becoming prohibitive for ...

Certain hypotheses can be tested using Student's t-test (maybe using Welch's correction for unequal variances in the two-sample case), or by a non-parametric test like the Wilcoxon paired signed rank ...

The data-generating-process is: $y = \text{sin}\Big(x+I(d=0)\Big) + \text{sin}\Big(x+4*I(d=1)\Big) + I(d=0)z^2 + 3I(d=1)z^2 + \mathbb{N}\left(0,1\right)$ Let $x,z$ be a sequence from $-4$ to $4$ of ...

How is a test task being nonparametric or parametric defined? My understanding is that the test task is parametric, if and only if it assumes a parametric model on the distribution of the sample, ...

I am trying to teach myself surface fitting with splines using tensor products. I am trying to construct a toy example but I can't seem to get my example to work. I will try to explain the best I can ....

I am using SPSS for generalized estimating equations including rather skewed variables. Now I am just curious as to how I should interpret and report my results. I have seen several papers using GEE ...

I am looking to pick an appropriate statistical model to generate an output similar to the ANOVA hourly wage example in https://en.wikipedia.org/wiki/Dummy_variable_(statistics). However, in my case: ...

I have two groups with very small sample sizes (just 6 obs per each group).It seems that There is a violation of normality and Homogeneity of variances (based on Plots). Now I was wondering if I can ...

Suppose I have a data set with three or more groups. After some exploratory data analysis, I find that The groups do not come from a normal distribution the variances within the groups are not equal,...

I am looking to perform a nonparametric test for trend on a continuous outcome across three groups, preferably in Python. For example height (pretend height is not normal) in 4th, 5th and 6th graders....

I am using Tweedie GLM as my data contains exact zeroes. However, my stats is weak and want to confirm a few things. Does Tweedie GLM assume normality of residuals? Is shapiro.test() the way for ...

I'm trying to find a model to predict a test score (0 to 100) from many independent ordinal variables (all are on a 1 to 10 scale). Since there are SO many ordinal variables, using binary dummy ...

I have a data set of an insect community composition (17 insect species, raw abundance data, sampled 5 times over 60 days within 24 tanks (4 replicates). There were 3 treatments (free fish, caged fish,...

Are there any good statistical non-parametric forecasting methods besides machine learning methods like neural networks/decision trees etc. for time series analysis ? If so, are there any R packages ...

I have read from several sources, even in my undergrad courses, that parametric tests require the data to have a certain distribution, for instance normal, whilst non-parametric don't. I have ...

I was playing around with the Rssa when I discovered this: Firstly: I created to sequences:library Rssa x<-1:100 x1<-1:80then the corresponding function:...

I use cross-validation, with random partitioning, to test the generalization power of a certain classifier (a short-sentences classifier). The problem is, my dataset contains many almost-identical ...

I am interested in estimating the gradient of the log probability distribution $\nabla\log p(x)$ when $p(x)$ is not analytically available but is only accessed via samples $x_i \sim p(x)$. There ...

There are several tests for randomness, one is runs up and down, another is run against mean or some other values, of cause, Bartel's rank test can also be used. There are studies shows where Bartel'...

I have five numeric variables of two populations (each of them with 60 individuals) and for each of those five variables I want to know if there is difference in the means. I was trying to use a ...

guys. I wanted to assess whether there is a significant difference between the means of three independent groups or not, but I cannnot use t test for them because the sample sizes are vastly different....

I want to run series of simple lm in R, with a continuous/categorical outcome and binary group membership (patients - controls) and categorical predictors. However the categorical predictor is ...

So, I have a very vexing theoretical question that I hope some experienced econometricians can help me with. Being in finance, I have recently been exposed to linear factor models, which are models ...

is it possible to simulate data, that follow: $Y_i = \sum_{j \neq i} w(i,j)\cdot Y_j +\epsilon_j $ So that $Y_i$ is a weighted average of all other $Y$'s? I'm thinking about this for a few days and ...

My study is related to the visual attractiveness of route-plans in a logistics context. In practice, route-plans are rejected based on the fact that they "do not look nice". I have conducted an ...

Apparently one can obtain a regression analysis as $$g(x)=\frac{\int yf(y,x)dy}{f(x)}$$ where $$f(x)=\int f(y,x)dy$$ is the marginal density of $X_i$. In effect, I believe, the above expression ...

In my dataset, I have five (ordinal) groups with an x-amount of measurement. Because homoscedasticity is violated, I performed the Friedman chi-square test to see if there are any statistical ...

I am looking to fit three regression functions $f_1, f_2, f_3:\mathbb{R}^2 \to \mathbb{R}$. For example, let's say $X_1$ is time, $X_2$ is geographic latitude, $f_1$ is the temperature, $f_2$ is the ...

I have no data now though, I'm learning nonparametric statistics now. As I learned so far, nonparametric test for three-level design is Kruskal-Wallis test. But Kruskal-Wallis test needs the equality ...

I am trying to compute the non parametric measure of sensitivity A' according to the following formula reported by Stanislav & Todorov (1999): $$ A'= .5+sign(H-F)*((H-F)^2+abs(H-F))/(4*max(H,F)-4*...

Let $(X_1,X_2,\cdots,X_n)$ be a random sample drawn from a population with distribution function $F$. Is the empirical distribution function $F_n$ the UMVUE of $F$? ( $F$ itself is the parameter of ...

I'm thinking about univariate density estimation. Original Question In parametric inference, you assume the data are generated from a density that can be summarized by finitely-many parameters. You ...

Let $X_i:= (X_{i,1}, ..., X_{i,k}) $ for $i =1...n$ be $\mathbb{R}^k$-valued random Variables with $k\geq2$. I wonder what the exact assumptions are to apply the Friedman test to Realisations of ...

Let $\{X_i\}_{i=1}^n$ be i.i.d. discrete random variables. Let $f_n(x) = \frac{1}{n}\sum_{i=1}^n \mathbb{1}(X_i=x)$. I am interested in the asymptotic distribution of $$\sum_x (f_n(x)-f(x))^2$$ I've ...

I have data from blood samples collected from 20 patients before, during (at 30 min, 1 h, 2 h, 3 h, 4 h) and after a medical treatment (using the slightly different treatment protocols A and B). The ...

I'm trying to develop a (functional) model to predict the output of a computer simulation. I've run a small set of experiments, varying the 4 predictors $a$, $b$, $c$, and $d$ via LHS, and calculated ...

I have two samples that I want to verify that variances are equals in order to apply Wilcoxon rank sum test that assume that the variance are equals. Here a boxplot As you can see the variance ...

There's many good ways to learn a distribution $p_X$ of an r.v. $X$ over $k$ symbols given many i.i.d. samples $X_1,\ldots, X_n$. The simplest is to use the sample relative frequencies $\hat{f}_X$ as ...

Suppose we have $n$ equations with an integral of the form $\int_0^{x_i} F(z)dz = c_i,\ i=1,\ldots,n$ where $F(y)=\mathbb{P}(X \le y)$ is an unknown cumulative distribution function of a non-negative ...

From the documentation page for the R function dunnTest from the package FSA: Performs Dunn's (1964) test of multiple ...

I wish to see whether the average meal masses of two species are significantly different, and I have sample sizes of >800 for both. My Wilcoxon rank sum test returns a W = 330520 value - how should I ...

Our group took samples of contamination in sediments from around 20 non-related rivers, during 4 months. I want to know if contaminant (micrograms per liter) is related to river and/or time, thus my ...

I am looking at extremely non linear data for which the ARMA/ARIMA models do not work well. Though, I see some autocorrelation, and I suspect to have better results for non linear autocorrelation. 1/ ...

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