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| bio | website | statistik.lmu.de/~scheipl |
|---|---|---|
| location | Munich, Germany | |
| age | ||
| visits | member for | 2 years, 6 months |
| seen | yesterday | |
| stats | profile views | 102 |
PhD candidate at LMU Munich
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Nov 14 |
awarded | Yearling |
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Sep 28 |
awarded | Nice Answer |
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Jun 8 |
awarded | Constituent |
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Jun 8 |
awarded | Caucus |
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Nov 14 |
awarded | Yearling |
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Jun 3 |
comment |
Expected value of non-standard quadratic form Right, thanks for pointing that out. |
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Jun 2 |
awarded | Scholar |
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Jun 2 |
comment |
Expected value of non-standard quadratic form Thank you! You made it seem easy... |
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Jun 2 |
accepted | Expected value of non-standard quadratic form |
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Jun 2 |
comment |
Expected value of non-standard quadratic form @mpiktas: it is a good reference, I have it on my desktop. Unfortunately, it only has expressions for standard quadratic forms. Not to offend you, but I find the type of comment you made, i.e., referring somebody to a widely known and very general reference without knowing/checking that it actually contains information relevant to the problem inappropriate. All that does is clutter up the page... |
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Jun 2 |
asked | Expected value of non-standard quadratic form |
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Mar 23 |
answered | Logistic Regression in R (Odds Ratio) |
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Mar 18 |
answered | How to produce a CI for a value predicted in CART? |
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Mar 14 |
comment |
How to find relationships between different types of events (defined by their 2D location)? Peter Diggle has written a "model-based geostatistics". He also has an analysis of Lancashire crime data on this page: lancs.ac.uk/staff/diggle/MADE that might give you some good ideas. |
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Mar 14 |
answered | How to find relationships between different types of events (defined by their 2D location)? |
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Mar 14 |
comment |
How to find relationships between different types of events (defined by their 2D location)? Whether this is appropriate depends on what you already know or assume about your data generating process. Subsampling the data by region (i.e. take all points in some predefined smaller window) can be dangerous if it's not homogeneous (because using a different window would have changed your conclusions). Sampling the data without regard to positioning for a training set has the effect of "thinning out" the observed process and invalidates conclusions you might want to draw about e.g. the range of correlations between marks or clustering/repulsion processes. |
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Mar 10 |
comment |
Does Cox Regression have an underlying Poisson distribution? More generally, assuming constant hazard rates over fixed time intervals (known as a piecewise-exponential model) you can fit fairly flexible survival models in the form of poisson GLMs - if you add interactions between the piecewise constant baseline hazard and covariates, you can estiamte time-varying effects and move away from the proportionality assumption, for example. Sources: Michael Friedman "Piecewise Exponential Models for Survival Data with Covariates", Annals of Statistics N LAIRD, D OLIVIER "Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques" JASA |
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Mar 7 |
comment |
Equations in the news: Translating a multi-level model to a general audience "there is just nothing to understand here -- it is just a standard linear regression model" - teehee.... like that's any consolation for mathphobics. I take it you've never had the pleasure of teaching undergraduate courses in stats for, let's say, sociology or, god help me, communications majors. |
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Mar 3 |
comment |
How to obtain the values used in plot.gam in mgcv? @hskin96: Dunno. Maybe you're using a differnt version of mgcv? I tested my code with mgcv_1.7-2, you may need to change the at argument of trace for earlier versions..? |
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Mar 2 |
comment |
How to obtain the values used in plot.gam in mgcv? oops, I didn't see yours when I posted my answer. Well, it's a little more detailed anyway.... |
