# The Spatial Specificity Paradox in brain imaging, remedied with valid, infinitely-circular, inference

The most prevalent mode of inference in brain imaging is inference on supra-threshold clusters, with random field theory providing error guarantees. This introduces a spatial specificity paradox. The larger the detected cluster, the less we know on the exact location of the activation. This is because the null hypothesis tested is “no activation in the whole cluster” so the alternative is “at least on voxel is active in the cluster”. This observation is now new, but merely an implication of the random field assumption.

# Ranting on MVPA

The use of MVPA for signal detection/localization in neuroimaging has troubled me for a long time. Somehow the community refuses to acknowledge that for the purpose of localization, multivariate tests (e.g. Hotelling’s $T^2$) are preferable. Why are multivariate tests preferable than accuracy tests?

# A surprising result on the power of the t-test

In our recent contribution [1], just published in The American Statistician we revisit the power analysis of the t-test.

# Sampling as an Epidemic Process

Respondent driven sampling (RDS) is an approach to sampling design and analysis which utilizes the networks of social relationships that connect members of the target population, using chain-referral. It is especially useful when sampling stigmatized groups, such as injection drug users, sex workers, and men who have sex with men, etc. In our latest contribution, just published in Biometrics, Yakir Berchenko, Simon Frost and myself, take a look at RDS and cast the sampling as a stochastic epidemic. This view allows us to analyze RDS using the likelihood framework, which was previously impossible. In particular, this allows us to debias population prevalence estimates, and estimate the population size! The likelihood framework also allows us to add Bayesian regularization, debias risk estimates a-la AIC, or cross-validation, which were previously impossible, without the sampling distribution.

# Intro to dimensionality reduction

Gave a guest lecture on dimensionality reduction at Amir Geva’s “Clustering and Unsupervised Computer Learning” graduate course. I tried to give a quick overview of major dimensionality reduction algorithms. In particular, I like to present algorithms via the problem they are aimed to solve, and not via how they solve it.

# What is a pattern? MVPA cast as a hypothesis test

In our recent contribution [1], just published in Neuroimage we cast the popular Multi-Voxel Pattern Analysis framework (MVPA) in terms of hypothesis testing. We do so because MVPA is typically used for signal localization, i.e., the detection of “information encoding” regions.

# Almost-embarrassingly-parallel algorithms for machine learning

Most machine learning algorithms are optimization problems. If they are not, they can often be cast as such. Optimization problems are notoriously hard to distribute. That is why machine learning from distributed BigData databases is so challenging.

# Interactive Plotting with R

Efrat is a MSc. student in my group. She works on integrating advanced Multivariate Process Control capabilities in interactive dashboards. During her work she aquired an impressive expertise in interactive plotting with R, and D3JS.

# Quality Engineering Class Notes

Now that I am a member of the Industrial Engineering Dept. at Ben Gurion University, I am naturally looking into statistical aspects of Industrial Engineering. In particular process control. This being the case, I started teaching Quality Engineering. While preparing the course, I read the classical introductory literature and I felt it failed to convey the beauty of the field, by focusing on too many little details. I thus went ahead and wrote my own book, which can be found online.

# Disambiguating Bayesian Statistics

The term “Bayesian Statistics” is mentioned in any introductory course to statistics and appears in countless papers and in books, in many contexts and with many meanings. Since it carries different meaning to different authors, I will try to suggest several different interpretations I have encountered.

# ICML 2015

I have attended this week the ICML2015 conference in Lille France. Here are some impressions…

# Analyzing your data in the Amazon Cloud with R

If you want use R and: your data does not fit in your hard disk, or you want to do some ad-hoc distributed computations, or you need 256 GB of RAM for fitting your model, or you want your data to be accesible from anywhere in the world, or you heard about “AWS” and want to know how it may help your statistical needs… Then it is time to remind you of an old post of mine explaining how to setup an environment for data analysis with R in the AWS cloud.