A generalized Fellner-Schall method for smoothing parameter estimation with application to Tweedie location, scale and shape models

We consider the estimation of smoothing parameters and variance components in models with a regular log likelihood subject to quadratic penalization of the model coefficients, via a generalization of the method of Fellner (1986) and Schall (1991). In particular: (i) we generalize the original method to the case of penalties that are linear in several smoothing parameters, thereby covering the important cases of tensor product and adaptive smoothers; (ii) we show why the method's steps increase the restricted marginal likelihood of the model, that it tends to converge faster than the EM algorithm, or obvious accelerations of this, and investigate its relation to Newton optimization; (iii) we generalize the method to any Fisher regular likelihood. The method represents a considerable simplification over existing methods of estimating smoothing parameters in the context of regular likelihoods, without sacrificing generality: for example, it is only necessary to compute with the same first and second derivatives of the log-likelihood required for coefficient estimation, and not with the third or fourth order derivatives required by alternative approaches. Examples are provided which would have been impossible or impractical with pre-existing Fellner-Schall methods, along with an example of a Tweedie location, scale and shape model which would be a challenge for alternative methods

THE ANTHROPOMETRY OF BODY ACTION

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73105/1/j.1749-6632.1955.tb32112.x.pd

The adaptive chin. By E. Lloyd DuBrul and Harry Sicher. (American Lecture Series No. 180.) 97 pp. Charles C Thomas, Springfield, Ill. 1954. $3.50

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/37458/1/1330140124_ftp.pd

Average of trial peaks versus peak of average profile : impact on change of direction biomechanics

The aims of this study were twofold: firstly, to compare lower limb kinematic and kinetic variables during a sprint and 90° cutting task between two averaging methods of obtaining discrete data (peak of average profile vs. average of individual trial peaks); secondly, to determine the effect of averaging methods on participant ranking of each variable within a group. Twenty-two participants, from multiple sports, performed a 90° cut, whereby lower limb kinematics and kinetics were assessed via 3D motion and ground reaction force (GRF) analysis. Six of the eight dependent variables (vertical and horizontal GRF; hip flexor, knee flexor, and knee abduction moments, and knee abduction angle) were significantly greater (p ≤ 0.001, g = 0.10-0.37, 2.74-10.40%) when expressed as an average of trial peaks compared to peak of average profiles. Trivial (g ≤ 0.04) and minimal differences (≤ 0.94%) were observed in peak hip and knee flexion angle between averaging methods. Very strong correlations (ρ ≥ 0.901, p <0.001) were observed for rankings of participants between averaging methods for all variables. Practitioners and researchers should obtain discrete data based on the average of trial peaks because it is not influenced by misalignments and variations in trial peak locations, in contrast to the peak from average profile

A population-based approach to background discrimination in particle physics

Background properties in experimental particle physics are typically estimated using control samples corresponding to large numbers of events. This can provide precise knowledge of average background distributions, but typically does not consider the effect of fluctuations in a data set of interest. A novel approach based on mixture model decomposition is presented as a way to estimate the effect of fluctuations on the shapes of probability distributions in a given data set, with a view to improving on the knowledge of background distributions obtained from control samples. Events are treated as heterogeneous populations comprising particles originating from different processes, and individual particles are mapped to a process of interest on a probabilistic basis. The proposed approach makes it possible to extract from the data information about the effect of fluctuations that would otherwise be lost using traditional methods based on high-statistics control samples. A feasibility study on Monte Carlo is presented, together with a comparison with existing techniques. Finally, the prospects for the development of tools for intensive offline analysis of individual events at the Large Hadron Collider are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser. Minor changes have been made to the text with respect to the published article with a view to improving readabilit

Are Optically-Selected Quasars Universally X-Ray Luminous? X-Ray/UV Relations in Sloan Digital Sky Survey Quasars

We analyze archived Chandra and XMM-Newton X-ray observations of 536 Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) quasars (QSOs) at 1.7 <= z <= 2.7 in order to characterize the relative UV and X-ray spectral properties of QSOs that do not have broad UV absorption lines (BALs). We constrain the fraction of X-ray weak, non-BAL QSOs and find that such objects are rare; for example, sources underluminous by a factor of 10 comprise \la2% of optically-selected SDSS QSOs. X-ray luminosities vary with respect to UV emission by a factor of \la2 over several years for most sources. UV continuum reddening and the presence of narrow-line absorbing systems are not strongly associated with X-ray weakness in our sample. X-ray brightness is significantly correlated with UV emission line properties, so that relatively X-ray weak, non-BAL QSOs generally have weaker, blueshifted CIV$\lambda$1549 emission and broader CIII]$\lambda$1909 lines. The CIV emission line strength depends on both UV and X-ray luminosity, suggesting that the physical mechanism driving the global Baldwin effect is also associated with X-ray emission.Comment: Accepted to Ap

Quantum homodyne tomography with a priori constraints

I present a novel algorithm for reconstructing the Wigner function from homodyne statistics. The proposed method, based on maximum-likelihood estimation, is capable of compensating for detection losses in a numerically stable way.Comment: 4 pages, REVTeX, 2 figure

Fast non-negative deconvolution for spike train inference from population calcium imaging

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm is fast enough that even when imaging over 100 neurons, inference can be performed on the set of all observed traces faster than real-time. Performing optimal spatial filtering on the images further refines the estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure

An Algorithmic Approach to Missing Data Problem in Modeling Human Aspects in Software Development

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem