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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Minimizing MISE to find consistent estimator
Consider kernel regression estimation of the mean function $m$ of the process
$$y_t = m(x_t) + \epsilon_t,$$ where $\epsilon_t$' s are correlated with covariance function $R(s,t) = \exp \{-\lambda|s-...
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Estimate fraction of a known distribution in a mixture with unknown second distribution
Suppose I have a set of bulbs, which are known to be healthy. For each bulb I have a value of its brightness. The underlying distribution is not necessarily normal, and possibly have some complex ...
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Non-uniform p-values from hoeffd function in Hmisc when data sets are independent
When using the function hoeffd in the CRAN package Hmisc I get unusual p-values for pairs of data sets that are independent. The function hoeffd is an implementation of Hoeffding's $D$ statistic. ...
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Estimating Spline curve by OLS. Is a good idea to fix the knots at Chebyshev sites?
I am writing my master's degree thesis on a novel method for fixing knots in an adaptive way and while reading the literature I've found many references to the so-called Chebyshev sites. This sites or ...
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Interpolation and Sample size when Visualizing distributions
Let's assume a stochastic simulation or test with a control variable. The task is to visualize the distribution to demonstrate the effect that is being researched. The objective is to get smooth plot, ...
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All vs all post-hoc after Aligned Friedman (k classifiers over multiple datasets)
I have k classifiers and n datasets, and I have only one accuracy measurement (which is actually the average of three independent repetitions of the 5-fold-CV, i.e. average over 15 accuracy values) ...
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Are these two estimated regression coefficient asymptotically equivalent? If not, which one is more efficient?
Suppose I have $Y=\beta_1X_1+\beta_2X_1X_2+g(X_2)+u$, where $E(u|X_1,X_2)=0$ and $S=g(X_2)+e$ with $E(e|X_2)=0$. I have a random sample $\{Y_i,X_{1i},X_{2i},S_i\}_{i=1}^n$. Suppose I first use a ...
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How can I make a prediction interval for a future response (not its mean) in regression by using bootstrap?
I'd like to know how I can use bootstrap to predict the confidence interval for a future response (not for its mean) no matter what theorical model and error distribution are, I know I can train the ...
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How to better understand when to use Weibull AFT versus Cox Model for Failure Data
I am struggling to understand when I should consider using a Cox regression model versus using a Weibull AFT model to predict the end of life of mechanical components.
I have tried to apply the Cox ...
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How general is the backfitting algorithm?
Hastie \& Tibshirani's original approach to fitting generalized additive models was the backfitting algorithm. For a model of the form
$$
y = \alpha + \displaystyle\sum_k f_k(x_k) + \epsilon
$$
...
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