Apr 12, 2021
2 mins
Tag:
__modeling__
In my previous blog post on hierarchical stacking, reader “Chaos” pointed to me Gavin Brown’s Ph.D. thesis on Negative Correlation (NC) Learning which had a good characterization of the importance of diversity to stacking or...

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Apr 07, 2021
3 mins
Tag:
__zombie__
__teaching__
I get an interview feedback from a company. I initially thought my interviews went well but it turned out that the company had a different opinion. Generally, it would be silly for me to post...

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Mar 03, 2021
3 mins
Tag:
__modeling__
The term online update here is referred to updating a statistical model after certain modeled outcome is observed. A concrete example is in election forecast: the state election result comes in sequence, and that is...

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Mar 01, 2021
1 min
Tag:
__modeling__
__computing__
In a recent paper I wrote, I discussed a few open questions on ensemble methods: Both BMA and stacking are restricted to a linear mixture form, would it be beneficial to consider other aggregation forms...

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Mar 01, 2021
3 mins
Tag:
__zombie__
Recently I have gone through a few industry job applications. Here is a few examples that manifest how amusing this procedure can become

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Feb 23, 2021
2 mins
Tag:
__modeling__
Depends who you ask.

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Feb 23, 2021
5 mins
Tag:
__causal__
I was reading Peter Bühlmann’s statistical science article “Invariance, Causality and Robustness”. To be fair, he gave a short course in 2020 here in Columbia, but after reading this paper I guess I did not...

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Feb 20, 2021
1 mins
Tag:
__computation__
It comes a lot that we often summarize a continuous distribution (often, posterior distribution of parameter estimation or of predictions) by a point mass (or a sharpe spike) for (1) computation or memory cost, (2)...

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Feb 19, 2021
7 mins
Tag:
__modeling__
__causal__
Cramér–Rao lower bound I will not call myself a theoretic statistician but sometimes I still find mathematical statistics amusing especially when they have practical implications. To start this blog post, I will go from Cramér–Rao...

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Dec 16, 2020
9 mins
Tag:
__modeling__
The likelihood principle is often phrase as an axiom in Bayesian statistics. My interpretation of the likelihood principle reads:

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Nov 24, 2020
4 mins
Tag:
__computation__
This comes a lot in Monte Carlo computation: we are only given finite draws but we want to compute extreme quantiles.

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Nov 21, 2020
2 mins
Tag:
__book__
Today I was reading the book “Discrete Distribution” by Johnson and Kotz. I did not realize it has a newer version until I started this blog post—-the edition I read was published in 1969 by...

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Oct 21, 2020
1 mins
Tag:
__modeling__
This is wrong. Indeed it can be opposite.

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Sep 28, 2020
2 mins
Tag:
__modeling__
In a predictive paradigm, no one really cares about how I obtain the estimation or the prediction. It can come from some MLE, MAP of risk minimization, or some Bayes procedure. Also, when we talk...

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Sep 16, 2020
3 mins
Tag:
__modeling__
in general B-spline is sensitive to the boundary knots, while the unknown support of latent variable models amplifies such sensitivity.

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