Data analysis with ordinal and interval dependent variables: examples from a study of real estate salespeople

This paper re-examines the problems of estimating the parameters of an underlying linear model using survey response data in which the dependent variables are in discrete categories of ascending order (ordinal, as distinct from numerical) or, where they are observed to fall into certain groups on a continuous scale (interval), where the actual values remain unobserved. An ordered probit model is discussed as an appropriate framework for statistical analysis for ordinal dependent variables. Next, a maximum likelihood estimator (MLE) derived from grouped data regression for interval dependent variable is discussed. Using LIMDEP, a packaged statistical program, survey data from an earlier manuscript are analyzed and the findings presented.

Contextual Bandit Learning with Predictable Rewards

Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on the action and context. We consider this problem under a realizability assumption: there exists a function in a (known) function class, always capable of predicting the expected reward, given the action and context. Under this assumption, we show three things. We present a new algorithm---Regressor Elimination--- with a regret similar to the agnostic setting (i.e. in the absence of realizability assumption). We prove a new lower bound showing no algorithm can achieve superior performance in the worst case even with the realizability assumption. However, we do show that for any set of policies (mapping contexts to actions), there is a distribution over rewards (given context) such that our new algorithm has constant regret unlike the previous approaches

A Contextual-Bandit Approach to Personalized News Article Recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.Comment: 10 pages, 5 figure

Volume-preserving normal forms of Hopf-zero singularity

A practical method is described for computing the unique generator of the algebra of first integrals associated with a large class of Hopf-zero singularity. The set of all volume-preserving classical normal forms of this singularity is introduced via a Lie algebra description. This is a maximal vector space of classical normal forms with first integral; this is whence our approach works. Systems with a non-zero condition on their quadratic parts are considered. The algebra of all first integrals for any such system has a unique (modulo scalar multiplication) generator. The infinite level volume-preserving parametric normal forms of any non-degenerate perturbation within the Lie algebra of any such system is computed, where it can have rich dynamics. The associated unique generator of the algebra of first integrals are derived. The symmetry group of the infinite level normal forms are also discussed. Some necessary formulas are derived and applied to appropriately modified R\"{o}ssler and generalized Kuramoto--Sivashinsky equations to demonstrate the applicability of our theoretical results. An approach (introduced by Iooss and Lombardi) is applied to find an optimal truncation for the first level normal forms of these examples with exponentially small remainders. The numerically suggested radius of convergence (for the first integral) associated with a hypernormalization step is discussed for the truncated first level normal forms of the examples. This is achieved by an efficient implementation of the results using Maple

Fast Neutron Detection with a Segmented Spectrometer

A fast neutron spectrometer consisting of segmented plastic scintillator and He-3 proportional counters was constructed for the measurement of neutrons in the energy range 1 MeV to 200 MeV. We discuss its design, principles of operation, and the method of analysis. The detector is capable of observing very low neutron fluxes in the presence of ambient gamma background and does not require scintillator pulseshape discrimination. The spectrometer was characterized for its energy response in fast neutron fields of 2.5 MeV and 14 MeV, and the results are compared with Monte Carlo simulations. Measurements of the fast neutron flux and energy response at 120 m above sea-level (39.130 deg. N, 77.218 deg. W) and at a depth of 560 m in a limestone mine are presented. Finally, the design of a spectrometer with improved sensitivity and energy resolution is discussed.Comment: 15 pages, 9 figures, published in NIM

Photometric Variability in Earthshine Observations

The identification of an extrasolar planet as Earth-like will depend on the detection of atmospheric signatures or surface non-uniformities. In this paper we present spatially unresolved flux light curves of Earth for the purpose of studying a prototype extrasolar terrestrial planet. Our monitoring of the photometric variability of earthshine revealed changes of up to 23 % per hour in the brightness of Earth's scattered light at around 600 nm, due to the removal of specular reflection from the view of the Moon. This variability is accompanied by reddening of the spectrum, and results from a change in surface properties across the continental boundary between the Indian Ocean and Africa's east coast. Our results based on earthshine monitoring indicate that specular reflection should provide a useful tool in determining the presence of liquid water on extrasolar planets via photometric observations.Comment: To appear in Astrobiology 9(3). 17 pages, 3 figures, 1 tabl

Rayleigh and depinning instabilities of forced liquid ridges on heterogeneous substrates

Depinning of two-dimensional liquid ridges and three-dimensional drops on an inclined substrate is studied within the lubrication approximation. The structures are pinned to wetting heterogeneities arising from variations of the strength of the short-range polar contribution to the disjoining pressure. The case of a periodic array of hydrophobic stripes transverse to the slope is studied in detail using a combination of direct numerical simulation and branch-following techniques. Under appropriate conditions the ridges may either depin and slide downslope as the slope is increased, or first breakup into drops via a transverse instability, prior to depinning. The different transition scenarios are examined together with the stability properties of the different possible states of the system.Comment: Physics synopsis link: http://physics.aps.org/synopsis-for/10.1103/PhysRevE.83.01630

Seasonal trends in concentrations and fluxes of volatile organic compounds above central London

Concentrations and fluxes of seven volatile organic compounds (VOCs) were measured between August and December 2012 at a roof-top site in central London as part of the ClearfLo project (Clean Air for London). VOC concentrations were quantified using a proton transfer reaction-mass spectrometer and fluxes were calculated using a virtual disjunct eddy covariance technique. The median VOC fluxes, including aromatics, oxygenated compounds and isoprene, ranged from 0.07 to 0.33 mg m−2 h−1 and mixing ratios were 7.27 ppb for methanol (m / z 33) and <1 ppb for the remaining compounds. Strong relationships were observed between most VOC fluxes and concentrations with traffic density, but also with photosynthetically active radiation (PAR) and temperature for the oxygenated compounds and isoprene. An estimated 50–90 % of aromatic fluxes were attributable to traffic activity, which showed little seasonal variation, suggesting boundary layer effects or possibly advected pollution may be the primary causes of increased concentrations of aromatics in winter. PAR and temperature-dependent processes accounted for the majority of isoprene, methanol and acetaldehyde fluxes and concentrations in August and September, when fluxes and concentrations were largest. Modelled biogenic isoprene fluxes using the G95 algorithm agreed well with measured fluxes in August and September, due to urban vegetation. Comparisons of estimated annual benzene emissions from the London and National Atmospheric Emissions Inventory agreed well with measured benzene fluxes. Flux footprint analysis indicated emission sources were localized and that boundary layer dynamics and source strengths were responsible for temporal and spatial VOC flux and concentration variability during the measurement period