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Statistics

A collection of 114 issues

Breaking the Monotony: Calibrating Without Preserving Monotonicity

Calibration refers to how well the predicted probabilities of an event match the actual frequencies of that event occurring. For instance, when a calibrated model predicts that an event has a 20% probability of occurring, that event occurs 20% of the time. The intuition extends to continuous values. When a

The GitHub Sampler

GitHub is the world's largest developer platform, making it a compelling place to build developer tools and study software development. However, with over half a billion repositories and an API that limits responses to a thousand results, it feels nigh impossible to answer even simple questions like, "

Coding At a Distance

Senior professors have long lived the future of coding. Professors ask. Underlings code. Professors critique the output, e.g., "I prefer statistical significance stars for the betas but not the constant term." Underlings revise the code. The cycle continues. Something similar can play out at companies. Now, thanks

Diabetes as a Dummy Variable

“Adults with diabetes are nearly twice as likely to have heart disease or stroke as adults without diabetes.” NIH These alarming numbers are used to justify more pre-emptive testing, e.g., Calcium score for the heart, more aggressive use of statin therapy, etc., among diabetics. The analyses that underlie the

Take 80 mg Atorvastatin for Myalgia

Here’s a drug pamphlet with a table about side effects The same table can be found here: https://www.rxlist.com/lipitor-drug.htm#side_effects As you can see, for a range of conditions, the rate at which patients experience side effects is greater in the Placebo arm than

A Benchmark For Benchmarks

Benchmark datasets like MNIST, ImageNet, etc., abound in machine learning. Such datasets stimulate work on a problem by providing an agreed-upon mark to beat. Many of the benchmark datasets, however, are constructed in an ad hoc manner. As a result, it is hard to understand why the best-performing models vary

Smallest Loss That Compute Can Buy

With Chris Alexiuk and Atul Dhingra The most expensive portion of model training today is GPU time. Given that, it is useful to ask what is the best way to spend the compute budget. More formally, the optimization problem is: minimize test loss given a FLOPs budget. To achieve the

Out of Network: The Tradeoffs in Using Network Based Targeting

In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month

Sampling Domain Knowledge

Say that we want to measure how often people go to risky websites. Let’s assume that the measurement of risk is expensive. We have data on how often people visit each domain on the web from a large sample. The number of unique domains in the data is large,

Homing in on the Home Advantage

A recent piece on ESPNCricinfo analyses the DRS data and argues that cricket should do away with neutral umpires. I reanalyzed the data. If a game is officiated by a home umpire, we expect the following: 1. Hosts will appeal less often as they are likely to be happier with