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1500 questions
34
votes
1 answer

Is there Factor analysis or PCA for ordinal or binary data?

I have completed the principal component analysis (PCA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), treating data with likert scale (5-level responses: none, a little, some,..) as a continuous variable. Then, using…
user116948
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34
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3 answers

When is it appropriate to use an improper scoring rule?

Merkle & Steyvers (2013) write: To formally define a proper scoring rule, let $f$ be a probabilistic forecast of a Bernoulli trial $d$ with true success probability $p$. Proper scoring rules are metrics whose expected values are minimized if…
34
votes
7 answers

What is normality?

In many different statistical methods there is an "assumption of normality". What is "normality" and how do I know if there is normality?
A Lion
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34
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3 answers

How to decide which glm family to use?

I have fish density data that I am trying to compare between several different collection techniques, the data has lots of zeros, and the histogram looks vaugley appropriate for a poisson distribution except that, as densities, it is not integer…
34
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3 answers

Is p-value a point estimate?

Since one can calculate confidence intervals for p-values and since the opposite of interval estimation is point estimation: Is p-value a point estimate?
34
votes
3 answers

(Why) Has Kohonen-style SOM fallen out of favor?

As far as I can tell, Kohonen-style SOMs had a peak back around 2005 and haven't seen as much favor recently. I haven't found any paper that says that SOMs have been subsumed by another method, or proven equivalent to something else (at higher…
Wayne
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34
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7 answers

Why is it bad to teach students that p-values are the probability that findings are due to chance?

Can someone please offer a nice succinct explanation why it is not a good idea to teach students that a p-value is the prob(their findings are due to [random] chance). My understanding is that a p-value is the prob(getting more extreme data | null…
Patrick
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34
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2 answers

Degrees of freedom of $\chi^2$ in Hosmer-Lemeshow test

The test statistic for the Hosmer-Lemeshow test (HLT) for goodness of fit (GOF) of a logistic regression model is defined as follows: The sample is then split into $d=10$ deciles, $D_1, D_2, \dots , D_{d}$, per decile one computes the following…
34
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3 answers

How to find confidence intervals for ratings?

Evan Miller's "How Not to Sort by Average Rating" proposes using the lower bound of a confidence interval to get a sensible aggregate "score" for rated items. However, it's working with a Bernoulli model: ratings are either thumbs up or thumbs…
Peter Taylor
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34
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6 answers

Interpretation of Shapiro-Wilk test

I'm pretty new to statistics and I need your help. I have a small sample, as follows: H4U 0.269 0.357 0.2 0.221 0.275 0.277 0.253 0.127 0.246 I ran the Shapiro-Wilk test using R: shapiro.test(precisionH4U$H4U) and I got the…
34
votes
3 answers

Coordinate vs. gradient descent

I was wondering what the different use cases are for the two algorithms, Coordinate Descent and Gradient Descent. I know that coordinate descent has problems with non-smooth functions but it is used in popular algorithms like SVM and LASSO. Gradient…
Bar
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34
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3 answers

What are the benefits of using ReLU over softplus as activation functions?

It is often mentioned that rectified linear units (ReLU) have superseded softplus units because they are linear and faster to compute. Does softplus it still have the advantage of inducing sparsity or is that restricted to the ReLU? The reason I ask…
brockl33
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34
votes
7 answers

Inference vs. estimation?

What are the differences between "inference" and "estimation" under the context of machine learning? As a newbie, I feel that we infer random variables and estimate the model parameters. Is my this understanding right? If not, what are the…
Sibbs Gambling
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34
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4 answers

What is the weak side of decision trees?

Decision trees seems to be a very understandable machine learning method. Once created it can be easily inspected by a human which is a great advantage in some applications. What are the practical weak sides of Decision Trees?
Łukasz Lew
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34
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3 answers

MSE decomposition to Variance and Bias Squared

In showing that MSE can be decomposed into variance plus the square of Bias, the proof in Wikipedia has a step, highlighted in the picture. How does this work? How is the expectation pushed in to the product from the 3rd step to the 4th step? If the…
statBeginner
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