All Questions
Tagged with nonparametric regression
170
questions
2
votes
0
answers
73
views
MCMC fitting of Dirichlet Process or Polya Tree prior to residuals in (simple linear regression)/(2-independent-samples) problem
Consider a simple location-shift semi-parametric model with two mutually-independent samples (in what follows, $F$ is a cumulative distribution function (CDF) on $\mathbb{ R }$, the $C_i$ and $T_j$ ...
- 69
13
votes
2
answers
2k
views
How to make predictions with non-parametric regression?
Let's say I have a dataset to which I have estimated a relationship using non-parametric regression, specifically Kernel (obviously in this hypothetical example it's probably overfit slightly). The ...
- 595
2
votes
2
answers
974
views
Bootstrap Standard Errors: should I divide the sampling standard deviation by $\sqrt{n}$?
Suppose I am bootstrapping an OLS regression and want the standard error of the coefficient $\beta_1$. I estimate the following regression on 1000 resamples of the data (where $B$ indexes the ...
- 103
0
votes
0
answers
106
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Nadaraya-Watson regression alternative for binary outcome
I am looking for pointers as to what would be the non-parametric equivalent of Nadaraya-Watson regression when modelling a binary outcome. I have been googling and ended up with Generalized Additive ...
- 363
2
votes
1
answer
760
views
How can I fit a regression for a variable which have a maximum value?
Let's suppose I have a test which gives me the dosage of an analytic in the blood. The results of the assay are in a range of 0 and 1000; all subjects who have a value higher than 1000 will be ...
- 686
0
votes
1
answer
29
views
A question about regressions and estimation
I have the following regression (all variables are vectors, i.e. its multiple-regression with $n$ responses and $m$ covariates)
$$Y = a + bX + \epsilon$$
So, $Y$ is $n$-dimensional response;
$a$ is $n$...
- 1
2
votes
1
answer
854
views
KNN as a crude prototype of Gaussian Process Regression?
I've heard it said before that K-Means-Clustering is a prototypical method for Expectation-Maximization algorithm. Where KM Clustering returns a hard cluster assignment, EM returns soft assignments, ...
- 3,382
1
vote
0
answers
354
views
Is it OK to use GEE insetad of GLM for non-repeated data?
I analyse data, that are repeated (pre-post), but I work with change from baseline instead. This is a non-randomized experiment, so I was advised to run this kind of analysis bearing in mind the Lord'...
- 11
1
vote
0
answers
32
views
What is a reasonable method for two sample test on cut-off measurements?
This is a question regarding biostatistics. I am digging into some statistical analysis with SingleCellSignalR, which is a tool to predict cellular interaction with single-cell RNA-seq data. I was ...
- 11
6
votes
2
answers
296
views
Regression with flexible functional form
I am assuming a model of the form
$$Y_i=\alpha+\beta X_i+g(\mathbf{Z}_i)+\epsilon_i,$$
here $\mathbf{Z}_i$ is an $m$ dimensional vector and $\epsilon_i$ is i.i.d. white noise. I would like to ...
- 340
0
votes
0
answers
31
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Biase of ASE estimation Kernel Regression
I'm trying to calculate the bias of the estimator $p(h)=n^{-1}\displaystyle\sum_{i=1}^{n}(Y_{j}-\hat{m}_{h}(X_{j})^{2}w(X_{j})$ of the averaged squared error. The result I find in the literature is ...
- 3
1
vote
0
answers
734
views
Alternating Conditional Expectations: Multiple regression transform
Alternating Conditional Expectations (ACE) is a non-parametric algorithm for multiple regression transform selection. It finds a set of transformed response variables that maximizes $R^2$ using ...
- 679
0
votes
1
answer
113
views
Nonparametric Regression
Suppose I have response y, continuous independent variable x and binary variable z.
...
- 357
0
votes
1
answer
76
views
How to understand the language used to describe statistics in the book "The Rise and Decline of Nations"
The tables on pages 102 - 108 are supposed to compare the growth rate and union membership between confederate and non-confederate states since 1965 (I believe it's since 1965 but it's so confusing I ...
- 103
1
vote
1
answer
810
views
How can I predict a continuous outcome variable with 1 binary and 1 ordinal predictor variable nonparametrically?
I have a binary predictor variable (situation1, situation2) and another one that's ordinally scaled (levels in an n-back task, they're called no-back,0-back,1-back & 2-back with 2-back being the ...
- 11