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Questions tagged [parametric]

Statistical models described by a finite number of real-valued parameters. Often used in contrast to non-parametric statistics.

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Patton's Symmetric Joe-Clayton copula

I am currently trying to apply Patton's Symmetric Joe-Clayton Copula, described in his "Modelling Asymmetric Exchange Rate Dependence". I am currently looking for the closed-form relation (if there is ...
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Covariance matrix in Gompertz paramteric survival model [on hold]

I would like to ask R for the covariance matrix of this Gompertz survival model: ...
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Confidence regions after fitting a 2 parameter gaussian mixture model?

Suppose I have a gaussian mixture model with 2 parameters $(u,v)$ and 2 parts. The model is $P({x_i}|u,v)=uN(x_i|\mu_1^{i} = x_i^2/v,\sigma_1^{i}) + (1-u)N(x_i|\mu_2^{i} = x_i^3/2v^2,\sigma_2^i)$. ...
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85 views

Prediction of regression coefficients with XGBoost

I am doing survival analysis. There is a dataset of items (id, group_id, observed lifetime, censorship status), each item belongs to a certain group. Each item is ...
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1answer
52 views

Parametric or non parametric test

I want to compare trends of R&D expenditures before and after a crisis. I was planning to use a paired T-test or a non-parametric alternative. But, before of that, I tested the data for normality. ...
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How could I estimate the quantiles of an unknown CDF?

I have been given with a set of data, which is supposedly come from an unknown distribution F. And I am asked to propose a suitable parametric or nonmparametric estimator for quantiles q(α) =F⁻¹(α) ...
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How does variation in the effect size affects power?

Effect size The effect size affect the power of a statistical test. We typically summarizes the magnitude of the effect of a variable as a single number (which we call the effect size). To my ...
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Chi-Square 2x2 Contingency vs z-test for two proportions: parametric or non-parametric?

Chi-Square Test I think it is generally agreed upon that the Chi-square test (specifically, the chi-square test for a 2-by-2 contingency table) is a non-parametric test. (Though there is the ...
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Why is “condensed nearest neighbour” Parametric? [duplicate]

Definition of "condensed nearest neighbour", at training time it chooses the c "best" training examples (where c is a hyper-parameter), and at test time uses the usual KNN prediction but based only on ...
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Confused about the statistical tests to choose or any transformation to apply

I am new to the stackexchange, so please forgive me for my editing ignorance. I am confused and stuck about how to proceed further with my data. I have the following data ...
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Parametric vs non-parametric machine learning methods [duplicate]

I looked-up many references and websites and researched on how to determine if a method is between parametric or non-parametric. I came up with below definitions, A parametric algorithm has a fixed ...
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102 views

Why is the correlation coefficient parametric?

I am a little confused on the definition of "parametric". The book that I'm reading writes that "the correlation coefficient attempts to estimate a particular parameter in the Normal model for two ...
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Does statistical significance impact uplift calculation for marketing campaign

I'm trying to understand 2 things in relation to calculating uplift from a marketing email campaign: Impact of statistical significance Impact of highly skewed data on calculating uplift The ...
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74 views

Regression discontinuity - optimal bandwidth choice

I have a very basic question. I would like to implement a nonparametric RD but I have a Poisson outcome variable. I would like to select the proper bandwidth and my question is about which method to ...
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Sample size calculation for parametric test, with large effect expected, small numbers. Should I use parametric or non parametric tests?

I am trying to figure out what would be the best way to analyze data from a randomized double blind trial we conducted. We sought to find if two dosages of a drug were effective for a severe symptom ...
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is the PROCESS-test in statistics a parametric or non parametric test?

This might be a silly question. But my supervisor wants me to redo the analytical (spss) tests of my thesis, because my data is not normally distributed. I found that there is an alternative for the ...
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What is a good test for whether a sample is drawn from a particular parametric family against a generalized alternative

Suppose I have some large number n of draws from a strictly positive distribution that I believe to be a member of a particular parametric distributional family. I use the draws to estimate the ...
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118 views

Simulating the Posterior Density of a Transformed Parameters

I am reviewing an example (p. 180-181, Example 11.3 and 11.4) from All of Statistics by Larry Wasserman. The example intends to illustrate that the posterior can be found analytically and can be ...
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1answer
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Wilcoxon Test - non normality, non equal variances, sample size not the same

I know there are already a lot of posts out there, but I couldn't find this exact combination in any of them. Comparing two samples (Prices associated with men and with women), but I have neither the ...
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Can one use parametric regression tests (e.g., Pearson's R2) on summary statistics (that are normal) derived from non-normal data?

Can one use parametric linear regression tests (e.g., Pearson's R2) on summary statistics (e.g., mean and median) that are normal but derived from non-normal data? I am specifically dealing with mean ...
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comparison of two independent data of same location using statistical test

I have elevation values of a geographical area with their lattitude and longitude, derived from two different satellites. How can I compare these data using some statiscal test?
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1answer
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Is there a commonly accepted effect size parameter for pairs of Bernoulli processes?

Let $f(x|\rho)$ be the Bernoulli pmf with probability $\rho$ of success. \begin{align} f(x|\rho) = \left\{ \begin{array}{ll} \rho & x = 1 \\ 1-\rho & x=0 \end{array} \right....
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Repeated Measures ANOVA with two factors

I have 300 patients with heart problems (2 groups/types of problem) that have completed the questionnaire SF-36 (8 scales), before and after the surgery. Some of them have been attending sessions with ...
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1answer
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How is an RNN (or any neural network) a parametric model?

I'm going through this paper A Multi-Horizon Quantile Recurrent Forecaster. The authors state that: 3.1. Loss Function In Quantile Regression, models are trained to minimize the total ...
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3answers
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Confused on normality assumption

I know that the sampling distribution of the mean can be assumed to be normal if N>30, but does this have an implication on the "30" itself (the sample data)? I have three different time series with ...
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Derive probability distributions from i.i.d. Gumbel

I have a question on how to derive (if possible) the following probability distributions. Consider 3 random variables $(X,Y,Z)$ mutually independent and identically distributed. Specifically, $X$ is ...
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1answer
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Parametric tests for non-normal data?

I'm trying to polish my stats skills and it seems to me that you have either parametric test for normal data, or non-parametric tests for non-normal data. Looking at the t-test for instance, I don't ...
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Coefficient of variation (CV) of log-transformed data

I understand that with log-transformed data, the coefficient of variation (CV) on the original scale is equal to sqrt(exp(sigma^2)-1), where sigma is the standard ...
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4answers
154 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 ...
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2answers
37 views

Likelihood term in Bayesian inferencing versus the general definition

In general we say that the likelihood function is defined as some $L(\theta|x)$, so that it is a function over some parameters: $\theta$ given some data: $x$. That is, $\theta$ is free to vary whilst $...
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MLE Asymptotics of Two independent samples

Given two independent samples, parameterized by the same parameter yet with different distributions (for example, Exponential(lambda) and Gamma(lambda, 2)), under what conditions is the parametric MLE ...
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Dau Genton test - in grandmothers terms

tl;dr Can you explain the Dau Genton test in terms a median grandmother could understand? Background: So I am looking for an "in.chull" for multivariate, concave hull, and I was going through "...
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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 ...
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235 views

Is there a loss function when estimating a model using MCMC?

I am trying to understand how fitting a model using MCMC works. Is there a loss function that is optimized? Or is it simply a case of more draws from the distribution amount to a more complete ...
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Reference request for parametric bootstrapping theory [duplicate]

Where is a good reference for the theory behind parametric bootstrapping (use MLE estimates as parameters of a distribution,then simulate from that distribution using those estimates as true values)? ...
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How to calculate mortality rate or probability of death at time t for various parametric distributions, e.g. Weibull, exponential, log-normal

If I have lambda and gamma, can I estimate that the probability of death at time t, based on a Weibull distribution is: What are similar formulas for the exponential, Gompertz, log-normal, log-...
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For MVN, zero covariance implies independence. How about other distributions?

According to this page on wikipedia, if $X\sim N(\mu,\Sigma)$ with $\mu\in\mathbb R^2$ and $\Sigma\in\mathbb R^{2\times2}$ then we have $\textrm{Cov}(X_1,X_2)=0\implies X_1\perp\!\!\!\perp X_2$. Is ...
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Linear combo of normals is normal; how about other distributions?

We know that univariate normal distributions are independent only if their every linear combination is itself normal: $$\tag{1} Z_i\sim N(\mu_i,\sigma_i^2)\,(\forall i) \implies\\ \biggl( \textrm{...
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minimax estimator

In the lecture here https://www.stat.berkeley.edu/~yuekai/201b/lec6.pdf we have "minimaxity does not imply admissibility: a minimax estimator has the best worst-case performance, but its ...
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Nonlinear regression: improving parameter estimates

I'm running a nonlinear regression to estimate $\delta$ and $\alpha$ using the following model, where $X$, $Y$ and $Z$ are the variables: \begin{equation} Z = \left(\delta X^\alpha+(1-\delta)Y^\alpha\...
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Type of parameter of the chi-squared distribution

Chi-squared distribution $\chi^2(k)$ has parameter $k$. On the one hand, $k$ should be the shape parameter because chi-squared distribution is a special case of Gamma distribution: $\chi^2(k) \equiv ...
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Types of parameters of a parametric family of probability distributions

A statistical parameter is a quantity that indexes a family of probability distributions. Wikipedia has the following definition of a shape parameter: A shape parameter is any parameter of a ...
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Why do several (if not all) parametric hypothesis tests assume random sampling?

Tests like Z, t, and several others assume that the data is based on a random sampling. Why? Suppose that I'm doing experimental research, where I care much more for the internal validity than the ...
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1answer
80 views

Parametric survival with correlated predictors

I have observational data on censored failure data. I am trying to perform a Parametric Survival as a function of variable A and B, where A is the time spent under control strategy 1, and B is the ...
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3answers
214 views

Function of a sufficient statistic

It is well-known that a 1-1 function of a sufficient statistic is also sufficient for a parameter $p$. I am however confused by the consequence that if $T$ is sufficient for a parameter $p$, then a ...
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Does the definition of regular estimator depend on the rate of convergence? If not, should it?

The definition of regular estimator in my lecture notes is: Let $X_1^{(n)}, \dots, X_n^{(n)} \overset{iid}{\sim} P_n \sim \mathcal{P}(\Theta)$ where $\mathcal{P}(\Theta)$ is a regular parametric ...
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How to estimate the probability of team A with win percentage $P_a$ winning against team B with win percentage $P_b$?

Surely this sort of problem must have a name. Because it does not have a well defined answer, I am asking for (1) the name of this type of problem and (2) for general approaches to this question that ...
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1answer
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How can the parameters be unknown but the probability distribution be known?

In an article (Nelson & Katzenstein, 2014) I came across the following sentence: ... “a fixed model of the economy with known parameters (or sometimes unknown parameters with known probability ...
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1answer
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Is the $t$-test asymptotically nonparametric?

Wikpedia defines "parametric statistics" as: ...a branch of statistics which assumes that sample data comes from a population that follows a probability distribution based on a fixed set of ...
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What if half of your data is not normally distributed? [closed]

My experiment is to test the different diets (Pk, Hg, BYD & Control) in order to check the development of insect, what are the most preferred diets by insect. For this purpose, I used 3 parameters;...