Questions tagged [frequentist]

In the frequentist approach to inference, statistical procedures are assessed by their performance over a hypothetical long run of repetitions of a process deemed to have generated the data.

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Lost in multiple comparisons: is there a princepled way out?

Suppose I am running some well-powered factorial experiment with several treatments and several levels. I am interested in the effects of all of all levels vs. some baseline for each treatment. I am ...
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Does bayesians' critique to frequentists apply to themselves too? [closed]

I've been reading about bayesians versus frequentists, including articles in this forum (like this one). Key is of course the issue of "priors". The bayesian critique being that frequentists ...
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How can I use Bayesian statistics to test this particular hypothesis?

There is a set $R=\{r_1, r_2, ..., r_K\}$ of $N$ ranks (where $N>> K$). I test the hypothesis that the ranks in $R$ are not homogeneously distributed in $\{1, 2, ..., N\}$. As I am interested in ...
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Bayesian vs. Frequentist results question [closed]

A researcher computes both a frequentist confidence interval, and a Bayesian credible interval. After the computation, the researcher realizes that the credible interval is much more narrow than the ...
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Frequentist vs Bayesian and deterministic vs stochastic

So this is sort of a general/basic, likely dumb question. I'm hoping to get a general idea, to better guide what I search/read. How do these terms relate to each other. I know with Bayesian theory, ...
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What does “Parameters are fixed and data vary” in frequentists' term and “Parameters vary and data are fixed” in Bayesians' term exactly mean?

I hear the sentence in my question a lot, I kind of understand what it means but never have a clear picture of it. Hope to get the clear picture of what the sentence exactly mean.
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Reporting: using bayesian and frequentist statistics interchangeably in a study

What would you expect to read in a work's "data analysis" or "statistics" section if this used Bayesian and frequentist methods interchangeably? I used Bayesian regression, since ...
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Is Bayesian estimation useful for causal analyses?

Is Bayesian estimation useful for causal analyses? For analyses like randomized experiments or even observational studies of natural experiments, we want unbiased estimators of the causal effect (...
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Is Frequentist Inference Objective?

Bayesian statistics is criticized for being subjective, as it requires a prior distribution encapsulating the subjective befiefs of the observer. Frequentist statistics is commonly advertised as being ...
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Crossing Frequentism and Bayesian Analysis

Has anyone considered giving the posteriors of an analysis a sampling distribution and seeing where, methodologically, things could go from there? For details, check out: https://sdba-stats.weebly.com
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Generate binary classification data in python?

Is there a simple way to generate binary classification data in python? I'd like to specify $X$ input parameter, $[x_1,...,x_n]$, and generate a dataset such that the (overall) McFadden's pseudo $R^2$ ...
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“It was the correct play even though I lost”

*sorry if this isn't the right SE community, maybe it's more philosophical* You often hear this refrain in games like Poker or Hearthstone. The idea is that making play A this game resulted in a loss, ...
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Coin flipping: Relationship between Bayesian and Frequentist's point estimates

I have a (biased) coin that has an unknown Head probability $p\in(0,1)$. To point estimate $p$, say that I'm going to use two approaches. Approach 1. I can use the Bayesian inference technique. ...
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Statistical Significance: Can we apply the concept of Statistical Significance when reading the results of AI driven marketing?

Really need to know this from someone who really understands statistical significance. Can we apply Statistical Significance when reading the results of AI driven marketing? Challenge: Large ...
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Can Random Forest be considered as a Frequentist method?

I am very new to machine learning so I apologize if this is a silly or even a repetitive question. I am running a Random forest model in R and was just wondering whether this is a frequentist method ...
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Is the admissible minimax decision rule ever a randomized action in frequentist statistics?

Are randomized action as opposed to pure action ever an admissible minimax rule in frequentist statistics,
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Bayesian vs Frequentist Prediction Methods and Frequencey Garantees

After reading Larry Wassermans blog on the difference between Bayesian and Frequentist inference I started to appreciate that frequency guarantees can be desirable regardless of the inference method ...
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Frequentist approach to marginalize nuisance parameters

How would be a frequentistic approach to solve this problem? "We have a random machine that gives 0 or 1 with a hard-coded, fixed but unknown probability $p$. After 10 trials we have 5 "0&...
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Interpretation of sampling distribution as the main distinction between Bayesian and classical statistics (Leamer)

In Hendry et al. (1990) p. 187-188, Edward Leamer says: To me the essential difference between the Bayesian and a classical point of view is not that the parameters are treated as random variables, ...
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135 views

brms intercept only model runs very slow

I am trying to learn brms package for multilevel modeling. A reproducible code is as below: ...
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3answers
60 views

Is there a name for, or interpretation of, very high p values?

If I flip a coin 1000 times and get only one head, I may suspect that the coin is biased. One justification for this suspicion is that I am unlikely to get so few heads under the null hypothesis of ...
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Parameters Quantification in Bayesian and Frequentist Approach

I am looking at a lecture on Bayesian Statistics and Why Bayes it is mentioned that, say the data can be modelled with normal distribution, the frequentist approach is to keep the $\mu$ and $\sigma$ ...
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1answer
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De Finetti's Coherence Principle and Frequentist interpretation

So, without proof or citation, I often see that the Coherence Principle by de Finetti does not hold with Frequentist statistics. It is pretty easy to create examples of this fact. The exception ...
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Does there exist a Frequentist or Non-Bayesian solution to Gull's Lighthouse Problem?

Does there exist a Frequentist or ODE or Non-Bayesian solution to Gull's Lighthouse Problem which is correctly modeled with cauchy distribution? See The Lighthouse Problem and Dave Harris' answer to ...
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Would you say this is a trade off between frequentist and Bayesian stats?

I'm trying to review frequentist and Bayesian in parallel. Let's say we are doing the typical scenario of estimating the population mean. In frequentist stats, if sample size is large enough, we ...
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Does Bayesian statistics bypass the need for the sampling distribution?

Let's take the classic case where the population follows a normal distribution, observations are iid, and we want to estimate the mean of the population. In Frequentist stats, we calculate the ...
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What is the Frequentist definition of fixed effects?

Bolker (2015) writes on p. 313 that Frequentists and Bayesians define random effects somewhat differently, which affects the way they use them. Frequentists define random effects as categorical ...
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What is two tailed test actually testing?

I know the two-tailed test procedure, that's not the issue. I am wondering about the philosophy about it. The null hypothesis $ \mu = X $ makes sense to me, we assume a certain value for the ...
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How to interpet this equation? [closed]

Can I get some help on interpreting this equation? Is it saying which ever section separated by the commma is bigger would be the answer? How do you intepret Sigma pi= 1? Thanks!
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Is sequential probability ratio test bayesian or frequentist or both?

Is sequential probability ratio test bayesian or frequentist or both? Is there a way to implement a bayes decision rule with "sprt"?
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Is the interpretation of a “Compatibility Interval” (Greenland, 2019) valid in general?

I was reading this short paper by Gelman and Greenland, where Greenland proposes to use the term "compatibility interval" instead of "confidence interval" with the following interpretation: [...] ...
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Understanding an example of using Bayesian and Frequentist inference

I have problems to understanding the following discussion. The questions are: 1)In "Some computation shows that this rule had probability 0.083,..." how $0.083$ calculated?(In a different version ...
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How to estimate parameters of a hierarchical model?

I have the following hierarchical model, where $t$ stands for time and $y_t, x_{1t}, x_{2t}, \dots, z_{1t}, z_{2t}, \dots$ are known. I want to estimate the parameters of the model using a ...
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AB/Hypothesis testing in multiple groups

Let's say I have two variants of my website and I would like to know which variant generates more revenue. This website operates in multiple countries and I will choose a variant for all countries at ...
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How to evaluating inconsistency and heterogeneity in a frequentist Network meta-analysis based exclusively on multi-armed studies?

I have been trying to perform a network meta-analysis on a set of 5 multi-armed trials, each having 3 arms (placebo, treatment 1, and treatment 2). I am using the network and network graphs packages ...
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Multiple test adjustments of p values: Data Mining vs Single tests

This discussion was triggered between me and a student after a significant p-value in a highly interesting correlation between two variables dissappeared after the p values were adjusted for multiple ...
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Typo in Deepleariningbook.org or am I misunderstanding Bayesian stats?

This is on page 133 of the book: https://www.deeplearningbook.org/contents/ml.html#pf10 In the above, it says that the data set is directly observed and so is not random If that data we observe ...
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Are two coin flips conditionally independent if we know that the coin is biased towards heads?

Suppose Alice (A) and Bob (B) each flip the same, potentially-biased coin. Then, P(A=H) < P(A=H | B=H), because Bob's flip increases our suspicion that the coin is biased towards heads. Now ...
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Could we move directly from frequentist interpretation $P(data|hypothesis)$ to Bayes interpretation $P(hypothesis|data)$ by using Bayes theorem?

Could we move directly from frequentist interpretation $P(data|hypothesis)$ result to Bayes interpretation $P(hypothesis|data)$ result by using Bayes theorem, or is it not legitimate ?
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Why would a Bayesian want to maximize expectation? [closed]

A Frequentist interprets probability as an estimate of how frequent an event is giving that we can repeat the experiment many times. It is natural for them to try to maximize the expected utility ...
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How to interpret a sampling distribution from a Frequentist and Bayesian perspective

I've read multiple of the threads about Bayesian vs Frequentist interpretations of probability, but I'm having trouble trying to reconcile them with the idea of the sampling distribution when ...
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Flaws in Frequentist Inference

I have problem to understanding the following example. (1) After the next day that the glitch discovered what can tell about the observation? $X_i\nsim N(\mu,1)$ or just $X_i\sim N(\mu_2,1)$. Some ...
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Is it possible to test if a parameter is positive?

Say I'm doing 1D linear regression. How can I test the hypothesis that the slope is positive? I have mostly been doing Bayesian analysis where this could be done easily by computing the proportion of ...
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1answer
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Compare Event frequency in two different data set

Assume we have two groups of categorical data: Group A: 10000 categorical events, such as Event1, Event2, Event3, ..., Event10 Group B: 100 categorical events, same events of group A, but recorded at ...
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Why is considering a maximum likelihood as a random variable a frequentist approach?

In the notes through which I'm working, the following is said: in order to compute the variance of a maximum likelihood estimator for $\mu$, $\hat{\mu}_{ML}$, we adopt a frequentist approach and ...
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Optimal decisions based on frequentist estimators

Consider a decision problem aimed at minimizing the expected loss1 where the argument is a parameter estimate. In a Bayesian setting, given a posterior distribution of the parameter and the loss ...
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41 views

Do frequentists rely less on entropy-based methods than Bayesian or Machine Learners? [closed]

From what I heard many times, Bayesian and Machine Learning people use entropy based methods. Do frequentists use entropy less?
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A reliance on repeated sampling ideas can lead to logical paradoxes that appear in common rather than esoteric procedures?

I am currently studying the textbook In All Likelihood -- Statistical Modelling and Inference Using Likelihood by Yudi Pawitan. Section Repeated sampling principle: the frequentists of chapter 1 says ...
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Why would considering $\theta$ to be a random variable not be 'Bayesian'?

I am currently studying the textbook In All Likelihood -- Statistical Modelling and Inference Using Likelihood by Yudi Pawitan. Section Inverse probability: the Bayesians of chapter 1 says the ...
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Why are frequentists uncomfortable with bayesian statistics when “optimization” algorithms used in frequentist statistics is bayesian?

In Step 1, we have a prior. Using bayes rule we construct the posterior. In step 2 of some iterated bayesian procedure, the prior becomes the posterior from step one and use bayes rule to calculate ...

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