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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Penalized spline confidence intervals based on cluster-sandwich VCV

This is my first post here, but I've benefited a lot from this forum's results popping up in google search results. I've been teaching myself semi-parametric regression using penalized splines. ...
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Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method

I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error. Setting: I have a sample S from a data population P and a learner L, ...
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Are there any surveys of the opinions of statisticians on the usefulness of classical rank-based nonparametric statistics?

The following comes from a YouTube video: Robustness in Statistics, which I have tried to quote verbatim. In Biology and Medicine these procedures are extremely popular, and I don't know why. They're ...
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What do the terms "nearly-optimal rate", "near-minimax rate", "minimax optimal rate" and "minimax rate" mean in the context of posterior consistency?

Definition: A sequence $\epsilon_n$ is a posterior contraction rate at the parameter $θ_0$ if $$\Pi_n(θ: d(θ, θ_0) ≥ M_n \epsilon_n| X^{(n)}) → 0$$ in $P^{(n)}_{θ_0}$-probability, for every $M_n → ∞$. ...
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What to do if your regression residuals aren't normally distributed, cannot be transformed and do not conform even when outliers are removed?

I ran a regression on R and my shapiro wilk test showed that some of my residuals are not normally dsitributed. I cannot transform the data to fit a normal distribution and even when i remove outliers,...
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Friedman's test to identify best of multiple classifiers on multiple domains

I have several classifiers $f_i\ (i=1, \cdots, N)$ and calculated performance measures on multiple domains $(D)$ for each. Thus, there are $N \times D$ values. I want to find out (increasing ...
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189 views

Non-parametric estimators for time-varying binomial proportion

I have a bunch of count data associated with time intervals (potentially overlapping and of variable lengths), say $(s_i, t_i, n_i, N_i)$ where $N_i$ is a count of the total number of events ...
• 316
7k views

Choosing post-hoc test after Kruskal-Wallis

I have a time series (five time steps) of samples from a population of the same ~60 individuals, each sample being a haphazardly chosen (i.e. not completely random) subset of the 60. The sample sizes ...
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Can a Gaussian Process predict random events?

I know that we can use Gaussian processes effectively for function approximation and regression. However,suppose there is a sequence of points in time $S = \{s_1, s_2, \dots, s_n\}$, where $s_i$ can ...
317 views

Estimation of Propensity Score using Random Forests

Suppose that one has a binary treatment $Z$, and assume that $Z=1|X=x \sim Bern\left(e(x)\right)$. Furthermore, suppose I want to estimate the propensity score by a random forest. Are there ...
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Pros and cons of Nadaraya–Watson estimator vs. RKHS method?

Recently I've been reading some materials about nonparametric methods. Two methods related to the word "kernel" rasied my interest-- Nadaraya–Watson estimator and RKHS method. What's the ...
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Looking for the Holy Grail of nonparametric regression

Unfortunately, to state precisely the question, I need some formal preliminaries. Let $d \in \mathbb{N}$. For each $d^* \in \{1,\dots,d\}$, define $\mathcal{M}_{d^*}$ be the set of probability ...
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What does it mean when F values of ANOVAs are not all ~0?

I am running an aligned rank transform ANOVA (a non-parametric ANOVA for an ordinal DV). In my data there are 3 factors and one ordinal DV. When I create the model: ...
428 views

Theoretical justification of Parametric bootstrap?

I've been reading about bootstrap, and while it's relatively easy to find theoretical results (consistency and higher-order correctness) for the nonparametric bootstrap (e.g., Asymptotic Statistics by ...
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The observations $Z_1,Z_2\cdots$ are i.i.d. We have $$Z_k = \sum_{i=1}^\infty \frac{X_{ki}}{2^k}.$$ where the $X_{ki}$'s are i.i.d. with a Bernouilli$(p)$ distribution. If $p=\frac{1}{2}$ then $Z_k$ ...