Regina C. Elandt

Bio: Regina C. Elandt is an academic researcher from Case Western Reserve University. The author has contributed to research in topics: Normal distribution & Folded normal distribution. The author has an hindex of 1, co-authored 1 publications receiving 98 citations.

The Folded Normal Distribution: Two Methods of Estimating Parameters from Moments

Abstract: The general formula for the rth moment of the folded normal distribution is obtained, and formulae for the first four non-central and central moments are calculated explicitly. To illustrate the mode of convergence of the folded normal to the normal distribution, as μ/σ = θ increases, the shape factors β f1 and β f2 were calculated and the relationship between them represented graphically. Two methods, one using first and second moments (Method I) and the other using second and fourth moments (Method II) of estimating the parameters μ and σ of the parent normal distribution are presented and their standard errors calculated. The accuracy of both methods, for various values of θ, are discussed.

The Skew-Normal and Related Families

Abstract: Preface 1. Modulation of symmetric densities 2. The skew-normal distribution: probability 3. The skew-normal distribution: statistics 4. Heavy and adaptive tails 5. The multivariate skew-normal distribution 6. Skew-elliptical distributions 7. Further extensions and other directions 8. Application-oriented work Appendices References.

The Impact of Trading Commission Incentives on Analysts' Stock Coverage Decisions and Earnings Forecasts

Abstract: In this paper, I consider how security analysts' incentives to generate trading commissions affect the accuracy and availability of their reports. I model the interaction between an analyst and an investor who holds an initial endowment of a risky asset and must pay a commission to a brokerage firm in order to buy or sell shares of the asset. The investor bases his decision on information provided by the analyst, who is in turn employed by the brokerage. The analyst's decision about how much information to gather about the asset is driven by his expectations of the commissions to be generated from that information. I find that the analyst's incentives to gather information are strongest for stocks that are expected to perform well; consequently, reports (i.e., forecasts) for these stocks are likely to be more accurate than forecasts

The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities

Abstract: The overlapping coefficient is defined as a measure of the agreement between two probability distributions. Its relationship to the dissimilarity index and its propertie are described. An extensive treatment of maximum-likelihood estimation of the overlap between two normal distributions is presented as an example of estimating the overlapping coefficient from sample data.

The geometric signature: Quantifying landslide-terrain types from digital elevation models

Abstract: Topography of various types and scales can be fingerprinted by computer analysis of altitude matrices (digital elevation models, or DEMs). The critical analytic tool is the geometric signature, a set of measures that describes topographic form well enough to distinguish among geomorphically disparate landscapes. Different surficial processes create topography with diagnostic forms that are recognizable in the field. The geometric signature abstracts those forms from contour maps or their DEMs and expresses them numerically. This multivariate characterization enables once-in-tractable problems to be addressed. The measures that constitute a geometric signature express different but complementary attributes of topographic form. Most parameters used here are statistical estimates of central tendency and dispersion for five major categories of terrain geometry; altitude, altitude variance spectrum, slope between slope reversals, and slope and its curvature at fixed slope lengths. As an experimental application of geometric signatures, two mapped terrain types associated with different processes of shallow landsliding in Marin County, California, were distinguished consistently by a 17-variable description of topography from 21×21 DEMs (30-m grid spacing). The small matrix is a statistical window that can be used to scan large DEMs by computer, thus potentially automating the mapping of contrasting terrain types. The two types in Marin County host either (1) slow slides: earth flows and slump-earth flows, or (2) rapid flows: debris avalanches and debris flows. The signature approach should adapt to terrain taxonomy and mapping in other areas, where conditions differ from those in Central California.