Calibration of Measurements

Traditional notions of measurement error typically rely on a strong mean-zero assumption on the expectation of the errors conditional on an unobservable “true score” (classical measurement error) or on the data themselves (Berkson measurement error). Weakly calibrated measurements for an unobservable true quantity are defined based on a weaker mean-zero assumption, giving rise to a measurement model of differential error. Applications show it retains many attractive features of estimation and inference when performing a naive data analysis (i.e. when performing an analysis on the error-prone measurements themselves), and other interesting properties not present in the classical or Berkson cases. Applied researchers concerned with measurement error should consider weakly calibrated errors and rely on the stronger formulations only when both a stronger model\u27s assumptions are justifiable and would result in appreciable inferential gains

Investigating the Performance of Propensity Score Approaches for Differential Item Functioning Analysis

To evaluate the performance of propensity score approaches for differential item functioning analysis, this simulation study was conducted to assess bias, mean square error, Type I error, and power under different levels of effect size and a variety of model misspecification conditions, including different types and missing patterns of covariates

Some Remarks on Rao and Lovric’s ‘Testing Point Null Hypothesis of a Normal Mean and the Truth: 21st Century Perspective’

Although we have much to agree with in Rao and Lovric’s important discussion of the test of point null hypotheses, it stirred us to provide a way out of their apparent Zero probability paradox and cast the Hodges-Lehmann paradigm from a Serlin-Lapsley approach. We close our remarks with an eye toward a broad perspective

Kakeya-type sets, lacunarity, and directional maximal operators in Euclidean space

Given a Cantor-type subset Ω of a smooth curve in ℝ(d+1), we construct random examples of Euclidean sets that contain unit line segments with directions from Ω and enjoy analytical features similar to those of traditional Kakeya sets of infinitesimal Lebesgue measure. We also develop a notion of finite order lacunarity for direction sets in ℝ(d+1), and use it to extend our construction to direction sets Ω that are sublacunary according to this definition. This generalizes to higher dimensions a pair of planar results due to Bateman and Katz [4], [3]. In particular, the existence of such sets implies that the directional maximal operator associated with the direction set Ω is unbounded on Lp(ℝ(d+1)) for all 1 ≤ p < ∞.Science, Faculty ofMathematics, Department ofGraduat

Measurement protocols, random-variable-valued measurements, and response process error: Estimation and inference when sample data are not deterministic.

Random-variable-valued measurements (RVVMs) are proposed as a new framework for treating measurement processes that generate non-deterministic sample data. They operate by assigning a probability measure to each observed sample instantiation of a global measurement process for some particular random quantity of interest, thus allowing for the explicit quantification of response process error. Common methodologies to date treat only measurement processes that generate fixed values for each sample unit, thus generating full (though possibly inaccurate) information on the random quantity of interest. However, many applied research situations in the non-experimental sciences naturally contain response process error, e.g. when psychologists assess patient agreement with various diagnostic survey items or when conservation biologists perform formal assessments to classify species-at-risk. Ignoring the sample-unit-level uncertainty of response process error in such measurement processes can greatly compromise the quality of resulting inferences. In this paper, a general theory of RVVMs is proposed to handle response process error, and several applications are considered

Evaluating UAV-based techniques to census an urban-nesting gull population on Canada's Pacific coast

The use of unmanned aerial vehicles, or drones, in wildlife monitoring has increased in recent years, particularly in hard-to-access habitats. We used fixed-wing and quadcopter drones to census an urban-nesting population of Glaucous-winged Gulls in Victoria, Canada. We conducted our study over 2 years and asked whether (i) drones represent a suitable survey method for rooftop-nesting gulls in our study region; and (ii) Victoria’s urban gull population had increased since the last survey >30 years earlier. Using orthomosaic imagery derived from drone overflights, we estimated at least a threefold increase over the 1986 count reported for the entire city (from 114 to 346 pairs), and an approximate tenfold increase in the number of gulls nesting in the downtown core. Drones proved to be an excellent platform from which to census rooftop-nesting birds: occupied nests were readily discernible in our digital imagery, and incubating birds were undisturbed by drones. This lack of disturbance may be due to Victoria’s location in an aerodrome; gulls experience dozens of floatplane and helicopter flights per day and are likely habituated to air traffic. Glaucous-winged Gulls have declined considerably at their natural island colonies in the region since the 1980s. Our results indicate that although urban roofs provide replacement nesting habitat for this species, local gull populations have not simply relocated en masse from islands to rooftops in the region.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Urban gulls living with humans

No abstract available