Biased and Inefficient

Derek Lowe, writing on models for protein-ligand interaction Ideally, you train your model on a big pile o’ protein data, while holding out a good piece of it to keep it out of the training set.

The almost-sure representation theorem in probability says that if \(X_n\stackrel{d}{\to}X\) we can find a sequence \(\tilde X_n, \tilde X\) with the same distributions as \(X_n,X\), possibly on a different probability space, so that \(\tilde X_n\stackrel…

As I have said before, the distinction between multistage and multiphase surveys is mostly important as a shibboleth for survey statisticians.

In one of the social media discussions about causal inference the suggestion was made that predictive models are all you need: a good predictive model gives you all the conditional distributions you could want, and you don’t need any special causal infere…

Version 4.5 of survey is on CRAN now.

This post is partly because I’m about to start teaching generalised linear models and partly to avoid doomscrolling Do predictive models need to be causal?

Attention Conservation Notice: I stole the title from Cosma Shalizi, who is a better writer than me.

Riffing off Andrew Gelman’s (correct) advice to just simulate, in both teaching and research, suppose I have a new robust location estimator.

Suppose we have a prediction problem.

Yes This question comes up from time to time on social media or StackExchange or email, often from reasonable people, so extra emphasis might be useful.

This is approximately my Belz Lecture for the Statistical Society of Australia (Vic/Tas), from a couple of weeks ago.

There’s increasing interest in (or, at least, coverage of) the idea that surveying large language models could substitute for surveying people.

There was a question on StackOverflow1 about continuous data with extra zeroes and comparing a two-part model to a continuous-only model using AIC.

Suppose you have a single transferable vote election to pick some number \(d\) of people, and that you belong to a large voting bloc of sheeplike followers or of cooperative forward-thinkers (delete as necessary).

The phrase asymptotically unbiased has at least two uses in statistics.

If you have two regression models \[E[Y|X]=\beta_0+\beta_1X\] and \[E[Y|X,Z]=\gamma_0+\gamma_1X+\gamma_2Z\] then typically \(\gamma_1\neq\beta_1\), because they are different things1 A common name for this phenomenon is omitted-variable bias.

The 2025 Ihaka Lectures are coming, in September this year.

Attention Conservation Notice: someone asked about this on social media, and I probably should have it written down for the advanced regression course, but you probably don’t need to know it.

People often ask what the difference is between strata and clusters in sampling.

The file iris_data.csv contains some data on iris photographs from unsplash.org.

As everyone knows, if you want to repeat a sequence of random numbers in statistical software1 you can reset your software’s random number stream to the same position and everything will deterministically repeat2.

Again with the ordinal comparisons, yes.

The piranha problem is both a metaphor and a set of theorems coming out of Andrew Gelman’s research group.

Quartets of datasets are trendy now, so here’s one for collinearity diagnostics.

The 1986 Joint Statistical Meetings had a Data Expo where participants were given a dataset to show off visualisation approaches.

The distribution of a \(n\times n\) quadratic form in Gaussian variables, \(X^TAX\) is a linear combination of \(\chi_1^2\) variables: \[Q\sim\sum_{i=1}^n\lambda_iZ^2_i\] where \(\lambda_i\) are the eigenvalues of \(A\Xi\) and \(\Xi\) is the covariance ma…

The National Land Transport Plan has just been released.

They’re back, again.

Attention Conservation Notice: Do you really care about multiparameter tests?

One of the projects from the R Spring and R Dev Day has been to add unequal sampling without replacement with specified marginal sampling probabilities to R’s sample().

The term “t-test” is a bit of a troll here, since I don’t mean either a test or the Normal-based inference developed by Gossett.

When two-phase study designs started being used in epidemiology and biostatistics there was a period of conflict.

In sandwich variance estimators, the middle of the sandwich1 is the variance of the estimating function.

The survey package returns many results as svystat objects, which are numeric vectors with variance matrix as an attribute (and other optional attributes).

The S3 method system only lets you dispatch methods on one argument of the generic.

Background The current development of the survey package now has an experimental implementation of cross-validation using replicate-weight decompositions of the data.

In dual-frame sampling you take two samples from overlapping sampling frames and you need to downweight people who could have been chosen in either frame so the overlap of the two frames isn’t counted twice.

I’ve mentioned before that mathematician Tim Gowers had run a ‘polymath’ (massively collaborative maths research) project on non-transitive dice.

[Updated: ran it with the right version of the code] I’m writing code for multiple-frame surveys in the survey package now, and it’s at the stage where the basic stuff works (Revision 337 from r-forge) though there’s quite a bit more to implement.

When I wrote about weights I mentioned that there was in some senses a fourth type of weights after sampling weights, precision weights, and frequency weights.

One problem in teaching statistics and communicating statistics and so on is “assumptions”.

Two interesting examples from my e-bike: The first is common in e-bikes.

‘Symbolically nested’ is a phrase I invented to distinguish two different types of nested model when writing my book about survey analysis.

With the long-awaited demise of stringsAsFactors=TRUE it’s now easier to use text strings in R.

If we have a domain or subpopulation \({\cal D}\) and want to estimate the mean of a variable \(Y\) in that domain, the usual survey estimator is \[\hat \mu_{\cal D}=\frac{\sum_{R_i=1} w_i Y_i I(i\in {\cal D})}{\sum_{R_i=1} w_i I(i\in {\cal D})}.\] That i…

Version 4.4-1 of the survey package for R is percolating through CRAN.

The DOOR outcome strategy – “Desirability Of Outcome Ranking” – is a relatively new approach to composite outcomes in clinical trials.

Suppose you find, by analysis of crime data, that a house which has been burgled once is more likely to be burgled again in the following few months.

Attention Conservation Notice: This is probably well known in some circles Suppose you have a (parametric, Normal) linear mixed model \[Y=X\beta+Zb+\epsilon\] where \(\epsilon\) are iid \(N(0,\sigma^2)\) and \(b\) are \(N(0, \sigma^2V(\theta))\).

Attention Conservation Notice: this is about multiparameter hypothesis tests, which are intrinsically not very interesting In regression models (and contingency tables) for survey data, there are two classes of tests based on a division that’s more or less