# Questions tagged [nonparametric-regression]

Nonparametric regression is a form of regression analysis where the form of the functional dependence of the response on the predictors is not assumed. It subsumes many kinds of models, like spline models, kernel regression, gaussian process regression, regression trees or random forrests, and others.

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1answer
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### R: “family” and “degree” specification in loess fitting

I can't understand the difference between the possible specifications of the family option in the loess command in R. This is ...
0answers
57 views

### why is the nadaraya watson estimator unbiased?

Say I have the model $Y_{i} = m(x_{i}) + \epsilon_{i}$ and $Y_{i}$ and $X_{i}$ are two mutually independent i.i.d. sequences. Then, how can I show that the Nadaraya Watson estimator is unbiased for ...
1answer
19 views

### Is the distinction between parametric and non-parametric statistics always clear-cut?

Is the distinction between parametric and non-parametric statistics always clear-cut or do examples of a statistic exists which cannot clearly assigned to one of these categories?
0answers
25 views

### can I estimate density function of 2d/3d data with kernel smoothing (e.g. ks package R), or are there better estimation methods

I have a 2d matrix of positive values (non integer), where the values can be thought of intensity at an x,y coordinate indexed by the row and column. I want to estimate a density function across this ...
1answer
25 views

### The nonparametric estimation in generalized regression model

Let $Y_t \in \mathbb{R}$ be a response variable and $X_t$ a $d$-dimensional explanatory variable. Assume we observe the process that $(X_1, Y_1), \cdots, (X_n, Y_n)$. \begin{equation} Y_{t} = \mu(...
1answer
144 views

### Gaussian and Epanechnikov Kernel Regressions giving drastically different estimations

sorry if this is the wrong place to be asking this question. I'm trying to implement kernel regression for a specific dataset I'm working with, but I'm noticing that the trendlines generated by my ...
1answer
38 views

### Nonparametric Regression

Suppose I have response y, continuous independent variable x and binary variable z. ...
1answer
33 views

### Can I use/make prediction/regression if my data is not normally distributes? Are non-parametric test for prediction?

My data is not normally distributed, and I`m confused what tests can I use (non-parametric, of course), but is there any way, to analyse prediction if the data is not normally distributed? I read ...
0answers
12 views

### About Generalized Additive Models - First parametric estimations, after nonparametric estimations for the remaining components

I wonder is it possible to construct a generalized linear modelin in that way, First, I will exclude the intercept term, which is standard for GAMs. Second, I will predict my interested dependent ...
1answer
37 views

### A particular method for estimating the gradient of a log-density from samples

Suppose I have $N$ samples $x^1, \ldots, x^N$ which were drawn iid from an unknown density $P(x)$. Suppose I am interested in estimating the vector-valued function $g(x) = \nabla \log P (x)$. One ...
0answers
20 views

### Examples for integration estimator

suppose I'm interested in estimating $C=\int_{a}^{b}g(x)dx$, where $a$ and $b$ are known, and $g(x)=E(Y|X=x)$ is an unknown function of $x$. The data I have is $\{Y_{i},X_{i}\}_{i=1}^{n}$, then a ...
0answers
12 views

### rate of convergence for cross derivative estimation in local linear regression

Suppose $Y_{i}=m(X_{1i},X_{2i})+\epsilon_{i}$, with $E(Y_{i}|X_{1i},X_{2i})=m(X_{1i},X_{2i})$ where $m(\cdot,\cdot)$ is an unknown smooth function. If the estimator $\widehat{m}(x_{1},x_{2})$ is ...
0answers
43 views

### Intuition of the regression model under fixed design case (nonparametric regression)

Let $(x_1,Y_1), \dotsc, (x_n,Y_n)$ be a random sample from the regression model $$Y_t=m(x_t)+\epsilon_t.$$ When authors want to develop the asymptotic properties of the local linear estimator of $m$ ...
1answer
45 views

### Rates of convergence for estimating population mean squared error

Suppose I have an i.i.d. sample $\{(Y_i, X_i)\}_{i=1}^n$ on which I am trying to estimate a conditional expectation model: $$Y = g(X) + \varepsilon,\quad \mathbb E[\varepsilon | X] = 0$$ There is a ...
1answer
127 views

### How robust is coxph when the proportional hazards assumption is violated?

How robust is the coxph when I don’t have proportional hazards? How common is non prop hazards and how do I fix it? Does transforming variables help? Does non parametric survival analysis handle non ...
0answers
74 views

### How Semiparametric regression works?

I am working on semiparametric regression models; $$y=\beta x_1 +m(x_2)+e$$. I can understand this combination of Parametric and Nonparametric but how to estimate the responses ($\hat y$)? What is ...
0answers
45 views

### Multiple regression for left-censored independent and dependent variables

I am interested in developing a predictive multiple regression model which predicts a concentration of one compound based on the measured concentrations of several other compounds. Both the dependent ...
1answer
104 views

### For non-parametric regression which one has better interpretation and properties, GAM or quantile regression?

As in the topic. I want to interpret data for which I have no clues about the distribution. It's neither count, percentage, continuous. I don't want any transformations. Instead I would like to ...
0answers
17 views

0answers
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### MSE of Non-parametric Regression (Theoretically)

I'm working through Computational Statistics, Second Edition by Givens and Hoeting and I cannot reproduce a result stated in the book. We're considering a nonparametric regression problem where the ...
0answers
263 views

### Question about the answer to “Local polynomial regression: Why does the variance increase monotonically in the degree?” [duplicate]

I appreciated Marco's elegant answer explaining why the variance of a local polynomial regression increases monotonically in the degree. However, in the end of the proof, I find difficult to calculate ...
1answer
768 views

### Rank and z-transform instead of Wilcoxon?

Andrew Gelman in a recent post in his blog suggests using a rank, transforming the rank to a z-score, and then using parametric tests and tools instead of performing non-parametric tests. I never ...
1answer
1k views

### R programming language: meaning of 'weights' parameter in library method 'loess'

I use the library method loess of the R programming language for non parametric data fitting. The dataset is two-dimensional. I have not found any proper documentation of the method parameter ...