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| bio | website | ventirisk.com |
|---|---|---|
| location | State College, PA | |
| age | 47 | |
| visits | member for | 5 months |
| seen | Apr 9 at 1:48 | |
| stats | profile views | 60 |
Arthur Small is co-founder and CEO of Venti Risk Management, a consultancy and software development firm based in State College, Pennsylvania, USA. His work focuses on the management of risks arising due to variations in weather and climate.
Before co-founding Venti, Small served as Associate Professor of Applied Economics and Finance in the Department of Meteorology at Penn State University; and as Assistant Professor in the Graduate School of Business and the School of International and Public Affairs at Columbia University. He holds a A.B. degree in mathematics from Columbia University, an M.S. in mathematics from Cornell University, and a Ph.D. in Agricultural and Resource Economics from the University of California at Berkeley.
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Mar 24 |
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Is a subset $B= {(-\infty,t),t\in \mathbb{R} }$ an event of sample space $\mathbb{R}$? @Zen is correct, and I was mistaken. I have edited my answer accordingly. |
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Mar 24 |
revised |
Is a subset $B= {(-\infty,t),t\in \mathbb{R} }$ an event of sample space $\mathbb{R}$? Amended answer to correct an error identified by another user; fixed grammar. |
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Mar 23 |
answered | Is a subset $B= {(-\infty,t),t\in \mathbb{R} }$ an event of sample space $\mathbb{R}$? |
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Feb 3 |
answered | Creating conditional probability distribution |
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Jan 30 |
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Determine likelihood that a list of N items contains no negative results after examining X items For an answer to a question that is formally similar to yours, see: stats.stackexchange.com/questions/47286/… |
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Jan 20 |
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Measuring representativeness of a (non-random) selection @Thilo: Oh, dear. I suppose I invited that follow-up, so have only myself to blame. Answering would, alas, take a long while. 'Any' stats textbook will cover the topic: see, e.g., W.H. Greene, Econometric Analysis. On 'why not?', I'll recommend a book I find canonical that covers the philosophical and practical problems of hypothesis testing quite well and in depth: Statistical Decision Theory and Bayesian Analysis by J.O. Berger, amazon.com/gp/product/1441930744. Very short answer: I'm interested in using stats to make better decisions under uncertainty. HT doesn't help. |
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Jan 17 |
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Measuring representativeness of a (non-random) selection There is a noteworthy economic literature on statistical measurement of race and sex discrimination in, e.g., labor markets, that might be useful. A seminal work is: The Statistical Theory of Racism and Sexism Edmund S. Phelps The American Economic Review Vol. 62, No. 4 (Sep., 1972), pp. 659-661 Published by: American Economic Association Article Stable URL: jstor.org/stable/1806107 |
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Jan 17 |
revised |
Measuring representativeness of a (non-random) selection In example: Replaced hand-waving approximate odds ratio with exact calculation; added brief proviso about election procedures for U.S. senators. |
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Jan 17 |
revised |
Measuring representativeness of a (non-random) selection Added interpretation of the numerical result as an odds ratio. |
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Jan 17 |
answered | Measuring representativeness of a (non-random) selection |
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Jan 16 |
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Minimizing variance of an estimator under sampling cost penalty @StasK: Touche$'$ :) |
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Jan 16 |
awarded | Tag Editor |
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Jan 16 |
revised |
value-of-information wiki excerpt added 138 characters in body |
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Jan 16 |
wiki | created value-of-information excerpt |
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Jan 16 |
suggested | suggested edit on value-of-information tag wiki excerpt |
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Jan 16 |
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Timeseries stationarity Differencing is most useful for analyzing series with unit roots, e.g., AR processes with coefficients equal to (or close to) unity. Consider the simplest such case in which $y_t$ follows a random walk, i.e., in which $y_t = y_{t-1} + e_t$. This process is non-stationary. However, the differenced series is stationary. Ref: en.wikipedia.org/wiki/Unit_root |
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Jan 16 |
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Minimizing variance of an estimator under sampling cost penalty Fixed statement of an equation. |
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Jan 16 |
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Minimizing variance of an estimator under sampling cost penalty @Jugurtha: My bad: I should have written $E[C \mid \cdot]$ as a double integral: $E[C \mid \cdot] = \int \int C(\hat{\theta},\theta) f(\hat{\theta} \mid x_{1:n}) d\hat{\theta} f({\theta} \mid x_{1:n}) d\theta$. That is, given a beliefs about the distribution of $\theta$, you are able to formulate expectations of how far off a given point estimate is likely to be. (I'll edit my original answer to fix.) |
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Jan 16 |
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R vs SAS, why is SAS prefered by private companies? @DimitriyV.Masterov: Revolution R is a commercial "enterprise" version of R that claims to allow R to run in-database. (I have not reviewed or audited this claim.) Revo R is not free ($1,000, I think), but its much cheaper than SAS. |
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Jan 15 |
revised |
Minimizing variance of an estimator under sampling cost penalty Fixed typo (had too many "nots") |
