## Archive for logistic regression

## moralizing gods drive Nature rejection

Posted in Statistics with tags applied Bayesian analysis, logistic regression, missing data, moralizing gods, Nature, R, religions, retraction on August 29, 2021 by xi'an## on approximations of Φ and Φ⁻¹

Posted in Books, Kids, R, Statistics with tags approximation, cdf, inverse cdf, Φ, logistic regression, numerical inversion, pnorm, Polya, qnorm(), R on June 3, 2021 by xi'an**A**s I was working on a research project with graduate students, I became interested in fast and not necessarily very accurate approximations to the normal cdf Φ and its inverse. Reading through this 2010 paper of Richards et al., using for instance Polya’s

(with another version replacing 2/π with the squared root of π/8) and

not to mention a rational faction. All of which are more efficient (in R), if barely, than the resident pnorm() function.

test replications elapsed relative user.self 3 logistic 100000 0.410 1.000 0.410 2 polya 100000 0.411 1.002 0.411 1 resident 100000 0.455 1.110 0.455

For the inverse cdf, the approximations there are involving numerical inversion except for

which proves slightly faster than qnorm()

test replications elapsed relative user.self 2 inv-polya 100000 0.401 1.000 0.401 1 resident 100000 0.450 1.000 0.450

## Metropolis-Hastings via Classification [One World ABC seminar]

Posted in Statistics, University life with tags ABC, ABC consistency, Chicago, Chicago Booth School of Business, classification, deep learning, discriminant analysis, GANs, logistic regression, Metropolis-Hastings algorithm, seminar, summary statistics, synthetic likelihood, University of Oxford, University of Warwick, webinar on May 27, 2021 by xi'an**T**oday, Veronika Rockova is giving a webinar on her paper with Tetsuya Kaji Metropolis-Hastings via classification. at the One World ABC seminar, at 11.30am UK time. (Which was also presented at the Oxford Stats seminar last Feb.) Please register if not already a member of the 1W ABC mailing list.

## reXing the bridge

Posted in Books, pictures, Statistics with tags bridge sampling, Charlie Geyer, computational physics, Elsevier, logistic regression, multi-armed bandits, normalising constant, reverse logistic, Statistica Sinica, umbrella sampling on April 27, 2021 by xi'an

**A**s I was re-reading Xiao-Li Meng’s and Wing Hung Wong’s 1996 bridge sampling paper in Statistica Sinica, I realised they were making the link with Geyer’s (1994) mythical tech report, in the sense that the iterative construction of α functions “converges to the `reverse logistic regression’ described in Geyer (1994) for the two-density cases” (p.839). Although they also saw the later as an “iterative” application of Torrie and Valleau’s (1977) “umbrella sampling” estimator. And cited Bennett (1976) in the Journal of Computational Physics *[for which Elsevier still asks for $39.95!]* as the originator of the formula [check (6)]. And of the optimal solution (check (8)). Bennett (1976) also mentions that the method fares poorly when the targets do not overlap:

“When the two ensembles neither overlap nor satisfy the above smoothness condition, an accurate estimate of the free energy cannot be made without gathering additional MC data from one or more intermediateensembles”

in which case this sequence of intermediate targets could be constructed and, who knows?!, optimised. (This may be the chain solution discussed in the conclusion of the paper.) Another optimisation not considered in enough detail is the allocation of the computing time to the two densities, maybe using a bandit strategy to avoid estimating the variance of the importance weights first.

## Metropolis-Hastings via classification

Posted in pictures, Statistics, Travel, University life with tags ABC, ABC consistency, Chicago, Chicago Booth School of Business, deep learning, discriminant analysis, GANs, logistic regression, seminar, summary statistics, synthetic likelihood, University of Oxford, webinar, winter running on February 23, 2021 by xi'an**V**eronicka Rockova (from Chicago Booth) gave a talk on this theme at the Oxford Stats seminar this afternoon. Starting with a survey of ABC, synthetic likelihoods, and pseudo-marginals, to motivate her approach via GANs, learning an approximation of the likelihood from the GAN discriminator. Her explanation for the GAN type estimate was crystal clear and made me wonder at the connection with Geyer’s 1994 logistic estimator of the likelihood (a form of discriminator with a fixed generator). She also expressed the ABC approximation hence created as the actual posterior times an exponential tilt. Which she proved is of order 1/n. And that a random variant of the algorithm (where the shift is averaged) is unbiased. Most interestingly requiring no calibration and no tolerance. Except indirectly when building the discriminator. And no summary statistic. Noteworthy tension between correct shape and correct location.