Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionF if he or she issues the probabilistic forecast F, rather than G ≠ F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest. In estimation problems, strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the ...

/pdf/strictly-proper-scoring-rules-prediction-and-estimation-28cdd1b5ww.pdf

Strictly Proper Scoring Rules, Prediction, and Estimation

SUMMARY
Over the last three years, a major international effort has been made by the Sub-Commission on Earthquake Algorithms of the International Association of Seismology and the Physics of the Earth's Interior (IASPEI) to generate new global traveltime tables for seismic phases to update the tables of Jeffreys & Bullen (1940). The new tables are specifically designed for convenient computational use, with high-accuracy interpolation in both depth and range. The new iasp91 traveltime tables are derived from a radially stratified velocity model which has been constructed so that the times for the major seismic phases are consistent with the reported times for events in the catalogue of the International Seismological Centre (ISC) for the period 1964–1987. The baseline for the P-wave traveltimes in the iasp91 model has been adjusted to provide only a small bias in origin time for well-constrained events at the main nuclear testing sites around the world.

For P-waves at teleseismic distances, the new tables are about 0.7s slower than the 1968 P-tables (Herrin 1968) and on average about 1.8–1.9 s faster than the Jeffreys & Bullen (1940) tables. For S-waves the teleseismic times lie between those of the JB tables and the results of Randall (1971).

Because the times for all phases are derived from the same velocity model, there is complete consistency between the traveltimes for different phases at different focal depths. The calculation scheme adopted for the new iasp91 tables is that proposed by Buland & Chapman (1983). Tables of delay time as a function of slowness are stored for each traveltime branch, and interpolated using a specially designed tau spline which takes care of square-root singularities in the derivative of the traveltime curve at certain critical slownesses. With this representation, once the source depth is specified, it is straightforward to find the traveltime explicitly for a given epicentral distance. The computational cost is no higher than a conventional look-up table, but there is increased accuracy in constructing the traveltimes for a source at arbitrary depth. A further advantage over standard tables is that exactly the same procedure can be used for each phase. For a given source depth, it is therefore possible to generate very rapidly a comprehensive list of traveltimes and associated derivatives for the main seismic phases which could be observed at a given epicentral distance.

/pdf/traveltimes-for-global-earthquake-location-and-phase-42n3pji8yf.pdf

Traveltimes for global earthquake location and phase identification

http://www.brainmapping.org/NITP/PNA/Readings/Poldrack2006.pdf

Can cognitive processes be inferred from neuroimaging data

of the number of bits required to write down the observed data, has been reformulated to extend the classical maximum likelihood principle. The principle permits estimation of the number of the parameters in statistical models in addition to their values and even of the way the parameters appear in the models; i.e., of the model structures. The principle rests on a new way to interpret and construct a universal prior distribution for the integers, which makes sense even when the parameter is an individual object. Truncated realvalued parameters are converted to integers by dividing them by their precision, and their prior is determined from the universal prior for the integers by optimizing the precision. 1. Introduction. In this paper we study estimation based upon the principle of minimizing the total number of binary digits required to rewrite the observed data, when each observation is given with some precision. Instead of attempting at an absolutely shortest description, which would be futile, we look for the optimum relative to a class of parametrically given distributions. This Minimum Description Length (MDL) principle, which we introduced in a less comprehensive form in [25], turns out to degenerate to the more familiar Maximum Likelihood (ML) principle in case the number of parameters in the models is fixed, so that the description length of the parameters themselves can be ignored. In another extreme case, where the parameters determine the data, it similarly degenerates to Jaynes's principle of maximum entropy, [14]. But the main power of the new criterion is that it permits estimates of the entire model, its parameters, their number, and even the way the parameters appear in the model; i.e., the model structure. Hence, there will be no need to supplement the estimated parameters with a separate hypothesis test to decide whether a model is adequately parameterized or, perhaps, over parameterized.

/pdf/a-universal-prior-for-integers-and-estimation-by-minimum-8qd1j2vu5a.pdf

A universal prior for integers and estimation by minimum description length

Some geometric properties of PD's are established, Kullback's $I$-divergence playing the role of squared Euclidean distance. The minimum discrimination information problem is viewed as that of projecting a PD onto a convex set of PD's and useful existence theorems for and characterizations of the minimizing PD are arrived at. A natural generalization of known iterative algorithms converging to the minimizing PD in special situations is given; even for those special cases, our convergence proof is more generally valid than those previously published. As corollaries of independent interest, generalizations of known results on the existence of PD's or nonnegative matrices of a certain form are obtained. The Lagrange multiplier technique is not used.

/pdf/i-divergence-geometry-of-probability-distributions-and-3ii060af2t.pdf

$I$-Divergence Geometry of Probability Distributions and Minimization Problems

Theory of Probability.

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