# Tag Info

1

James K. Lindsey has several books in similar vein. Perhaps the one nearest to your goal is http://www.amazon.co.uk/Introduction-Applied-Statistics-Modelling-Approach/dp/0198528957/

1

It's probably not quite what you have in mind, but Dan Navarro has written a textbook for his own undergraduate statistics subject in psychology. I have read it all, but it seems to use the approach you mention on occasion. It uses a fair bit of R and generally seems to be more sophisticated than your average intro psych stats textbook. The PDF of the ...

5

I'd not heard of the Barber book before, but having had a quick look through it, it does look very very good. Unless you've got a particular field you want to look into I'd suggest the following (some/many of which you've probably already heard of): Information theory, inference and learning algorithms, by D.J.C Mackay. A classic, and the author makes a ...

6

The problem you're talking about is known as "censoring" - specifically "right censoring" ("left censoring" is when the start time is unknown but the end time is known). The problem is endemic in the field of survival analysis. This set of notes gives details of the standard techniques for dealing with it, and contains a number of references to textbooks on ...

2

This book by J. Scott Long should suit your purposes.

1

Being a simple physicist, not a statistics expert, I'd take a simple approach. The two dimensions are of different natures. It would make sense to smooth along time with one algorithm, and smooth along wavelength with another. The actual algorithms I'd use: for wavelength, Savitzky-Golay with a higher order, 6 maybe 8. Along time, if that example is ...

1

I would recommend playing around with the statistics in R, if you're familiar with R. use arima.sim() to generate time series with different characteristics (order=c(1,0,0) for an AR(1) model) then you can use arima() or ar() functions to estimate the parameters. To graphically understand whats going on, use pacf() and acf() to plot the partial/ ...

1

I like to think of this as a problem in choosing representative or otherwise sensible values of x1 and x2 for a predictor X, and getting predicted Y when X=x2 minus predicted Y when X=x1. It is easy to get a confidence intervals for such a difference (in R this is a feature of the rms package). This handles nonlinearities in the X effect. I don't like to ...

1

One way to look at it is this. If you change the units of all independent variables (while keeping the same units for the dependent variable) then you should expect the regression coefficients to change. The smaller the units, implying larger values, the smaller the coefficients. No academic paper would make such a basic point, but if you need a ...

7

Whatever software you used was evidently reporting coefficients to 3 d.p. So 0.000 just meant <0.0005. It makes perfect sense to use units of measurement that yield coefficients that aren't inconveniently large or small. No statistical principle is violated thereby. You don't need a reference or authority to back this up: it is fine to choose (e.g.) mm ...

1

As far as I know, no such book exists yet as the area is still quite new. The couple Bayesian nonparametrics books I've seen are basically just a bunch of review papers from various researchers bound together. If you have a PhD in math, applied or not, I'm sure you can get your head around by reading the standard papers. Probably the gentlest yet most ...

0

If you have some familiarity with R, this youtube tutorial may be useful.

1

Sparse estimators are frequently used in a high dimensional context, namely $p>>n$. Essentially, they offer a regularized version of an estimator e.g. a least squares estimator with an $l_1$ or $l_0$ norm based parameter penalty. Informally, it promotes zeros in the solution set of the estimator. This is best understood as follows: Assume you ...

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In a master's thesis, you should probably explain a bit what the method does. In addition, you should somewhere give the details (package, version, function name, parameters). This may be all in the text, or shortly in the text and full details in an appendix. In papers, where text needs to be shorter, I e.g. say Data analysis was performed in R [1] ...

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If you just say "loess" people probably won't know what you mean. Perhaps "locally weighted regression" is better? The R help description of loess in stats is Fit a polynomial surface determined by one or more numerical predictors, using local fitting.

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