Procedures that rely on relatively few assumptions about underlying probability distributions.
0
votes
0answers
17 views
Models under Regression Analysis list [on hold]
I am compiling a list of models under Regression analysis(Whatever I think is useful for Machine learning) which is divided into two models i.e Parametric and Non-parametric Regression. Got most of ...
0
votes
0answers
10 views
non-normal censored outcome, high dimensional predictors
I have an outcome that is non-normal (multimodal) and has an upper limit of detection giving censored values at an upper bound. I have several hundred observations and wish to predict these non-normal ...
0
votes
0answers
26 views
Kolmogorov smirnov test [closed]
Is there any better algorithm than kolmogorav smirnov test ( peacock-2d and peacock-3d test) for user behavior anomaly detection in Multidimensional Data?
0
votes
0answers
9 views
Using abcnon bootstrap function on bivariate problem (regression) [closed]
The examples for the non-parametric bootstrap function "abcnon" in the R package, "bootstrap", are all univariate. How do you define the input function to use more than one variable. As an example:
<...
0
votes
0answers
21 views
Multivariate plug-in bandwidth estimator local constant regression
Has someone good references for multivariate plug-in bandwidth estimators in local constant regression? All I'm finding is for the univaraite case.
And is there maybe already an implementation in R? ...
1
vote
0answers
25 views
Determining the number of simulations in non-parametric classification simulation
I am currently working on setting up a simulation study that compares the effectiveness of k-nearest neighbours and gradient boosted trees.
I have checked these two sources. Both refer to p-values ...
0
votes
0answers
6 views
Nonparametric classification of a sample of values — is my approach correct?
Suppose I have a machine with a number of different labelled settings. The labels go from $1$ up to $L$. When I choose a setting on the dial, let's say setting $j$, I can have it output i.i.d. samples ...
1
vote
0answers
12 views
What among location, scale and shape is Kolmogorov–Smirnov test statistic sensitive to and why?
I understand that the Kolmogorov–Smirnov test statistic for a given cumulative distribution function $F(x)$ is $D_n = \sup_x |F_n(x) - F(x)|$. However, if I have to rank its sensitivity to location, ...
6
votes
1answer
86 views
Comparison of variance between two samples with unequal sample size
The primary goal of my analysis is to compare the variability in the response variable, Blood Pressure, between sample1 and sample2. The secondary goal is to test for a difference in means. I do not ...
0
votes
0answers
23 views
Non-parametric test of 3 samples
My classmate is working on a scientific investigatory project entitled ANTIMICROBIAL ACTIVITY OF UBE Discorea alata L. LEAVES PURE EXTRACT ETHANOLIC EXTRACT IN INHIBITING THE GROWTH OF Klebsiella ...
0
votes
0answers
15 views
Determining significance level in non parametric setting
I have a homework problem from a non parametric course.
Sample of size 25 is drawn from a symmetric distribution with mean=theta and variance=1.
For testing H0:theta=0 vs H1:theta>0, we reject H0 if ...
0
votes
0answers
13 views
Multiple Comparison in non-parametric setting?
I have the following problem: There are 4 independent non-Gaussian continuous distributions, A, B, C, D, and I want to see whether one of A and B is "superior" to both of C and D.
I'm aware of large ...
2
votes
2answers
49 views
Is it possible to calculate a confidence interval related to a significant p value from the kruskal wallis test?
I have run the nonparametric Kruskal Wallis test with pairwise comparisons in SPSS to understand if my 3 groups of survey participants were different in their responses to a range of 5 point ordinal ...
0
votes
0answers
25 views
Wilcoxon rank sum test confidence interval by hand
I'm trying to find a 91.8% exact confidence interval for a sample with sizes $m=n=8$. I know wilcox.test gives the confidence interval but my instructor has told us to do it by hand. The formula for
...
0
votes
0answers
7 views
Mann-Whitney-Wilcoxon-Test: should I transform my categorical data to have more realistic intervals?
I want to know whether old impaired people (75+) are more likely to use less the bus than younger impaired people. My respondents did not indicate the amount of days they travel by bus per year, but ...
0
votes
0answers
25 views
Goodness of fit test for my Data
We have one Original Set and one set of the same data after smoothing.
The smoothed data set has been generated through natural cubic spline regression over the original data, which is a non-...
0
votes
0answers
19 views
What are the implications of asymmetry in the Wilcoxon signed-rank test?
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 ...
0
votes
0answers
13 views
How to choose the correct sample and hypothesis test for spatiotemporal observations?
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 ...
0
votes
0answers
5 views
What happens to the Brown-Forsythe test when the data is weighted?
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 ...
2
votes
1answer
82 views
How to graph Wilcoxon test power R
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 ...
0
votes
0answers
14 views
Reporting values from generalized estimating equations with dependent variables having over two values
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 ...
0
votes
0answers
30 views
Non-parametric ANOVA alternative with 10 independent dichotomous variables
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:
...
1
vote
1answer
41 views
Dealing with very small samples with different shapes
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 ...
2
votes
2answers
73 views
Non normal residuals for Tweedie GLM
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 ...
1
vote
4answers
85 views
Why use parametric test at all if non parametric tests are 'less strict'
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 ...
0
votes
0answers
18 views
Non parametric test [duplicate]
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....
6
votes
3answers
435 views
Non-parametric alternative to simple t-test
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 ...
1
vote
0answers
12 views
Intuition: What is the difference between linear factor models and regular linear regression?
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 ...
1
vote
0answers
37 views
Data Simulation weighted average [closed]
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 ...
1
vote
1answer
33 views
Can I assess the relationship between a normally and non-normally distributed variable?
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 ...
1
vote
0answers
31 views
Smooth regression algorithms that produce zero training error
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 ...
0
votes
0answers
4 views
What test for 3-level * non-normality * non equality of variance (like Brunner-Munzel test for 2-level factor)
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 ...
1
vote
0answers
46 views
A' (A prime) for extreme Hit rates and False Alarms
I am trying to compute the non parametric measure of sensitivity A' according to the following formula reported by Stanislav & Todorov (1999):
$$
A'= .5+sign(H-F)*((H-F)^2+abs(H-F))/(4*max(H,F)-4*...
2
votes
1answer
71 views
UMVUE of distribution function $F$ when $X_i\sim F$ are i.i.d random variables
Let $(X_1,X_2,\cdots,X_n)$ be a random sample drawn from a population with distribution function $F$. Is the empirical distribution function $F_n$ the UMVUE of $F$? ( $F$ itself is the parameter of ...
3
votes
1answer
84 views
Is parametric Bayesian inference a special case of nonparametric Bayesian inference?
I'm thinking about univariate density estimation.
Original Question
In parametric inference, you assume the data are generated from a density that can be summarized by finitely-many parameters. You ...
1
vote
1answer
55 views
Assumptions of the Friedman Rank Test
Let $X_i:= (X_{i,1}, ..., X_{i,k}) $ for $i =1...n$ be $\mathbb{R}^k$-valued random Variables with $k\geq2$.
I wonder what the exact assumptions are to apply the Friedman test to Realisations of ...
0
votes
0answers
17 views
Group comparision in repeated measures data with missing values?
I have data from blood samples collected from 20 patients before, during (at 30 min, 1 h, 2 h, 3 h, 4 h) and after a medical treatment (using the slightly different treatment protocols A and B). The ...
0
votes
1answer
26 views
What is the asymptotic distribution of the integrated MSE of the histogram for a discrete random variable?
Let $\{X_i\}_{i=1}^n$ be i.i.d. discrete random variables. Let $f_n(x) = \frac{1}{n}\sum_{i=1}^n \mathbb{1}(X_i=x)$. I am interested in the asymptotic distribution of
$$\sum_x (f_n(x)-f(x))^2$$
I've ...
0
votes
1answer
48 views
Approximate a CDF
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 ...
0
votes
0answers
93 views
Dunn's test of multiple comparisons
From the documentation page for the R function dunnTest from the package FSA:
Performs Dunn's (1964) test of multiple ...
0
votes
1answer
211 views
Fligner-Killeen test of homogeneity of variances interpretation
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 ...
1
vote
2answers
44 views
2-ways anova for non-normal data
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 ...
-1
votes
1answer
28 views
mixing non-parametric correlation and parametric regression
I'm running both correlational and regression analyses on a variable that is not normally distributed:
For correlations, I decided to use Spearman's rank correlation (which is non-parametric) due to ...
0
votes
0answers
29 views
Multiple regression to maximize Kendall's tau
I've seen methods for maximizing Kendall's tau using regression with a single independent variable, e.g. Sen's 1968 article. I'm interested in fitting coefficients in a multiple linear regression ...
1
vote
1answer
28 views
How can I run multiple tests in a valid and random fashion?
I hope this is the correct place to ask this question. I'm currently doing research where I'm comparing two groups on their EEG frequency spectra. I wasn't too sure how to approach this.
Data:
I ...
0
votes
0answers
25 views
Non-parametric analysis for between-subjects
I performed 3 experiments, each experiment tested a different system, and each experiment involved a distinct group of subjects. In total there where 25 participants (7 for the first system, 7 for the ...
0
votes
0answers
15 views
Suitable non-parametric test for comparing mean between two group [duplicate]
I have a data of dependent variable(risky driving behaviour) and 3 categorical variables. In 2 categorical variable( gender and car ownership) have 2 group and 1 categorical variable (driving ...
0
votes
0answers
10 views
the informal definition of the almost complete convergence
I'm searching for the informal definition of the almost complete convergence (P. L. Hsu and H. Robbins (1947), Dugué (1955)), which means a definition without any mathematics formula.
Thank you for ...
0
votes
0answers
24 views
Kernel regression: Bandwidth selection on subsample
I have two columns of data which I sort by size (ascending) of the second column.
I now want to execute a kernel regression on the first column (Nadaraya-Watson estimator) and I am interested in only ...
0
votes
0answers
15 views
Comparing effect of two variables
I have two IV’s (A) and (B) and a DV (X). The hypothesis is: (A) has a higher propensity to result in (X) than (B).
X is Dichotomous.
A and B are single 5-point Likert-scale questions.
I’m ...
