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Stringent Assumptions (stringent + assumption)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	50%

Selected Abstracts

Modeling maternal-offspring gene-gene interactions: the extended-MFG test

GENETIC EPIDEMIOLOGY, Issue 5 2010
Erica J. Childs
Abstract Maternal-fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended-MFG (EMFG) Test, a model-based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear-family based "mating type" approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type-I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes. Genet. Epidemiol. 34: 512,521, 2010.© 2010 Wiley-Liss, Inc. [source]

Identification of Standard Auction Models

ECONOMETRICA, Issue 6 2002
Susan Athey
This paper presents new identification results for models of first,price, second,price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis of observed data. The simplest model,symmetric independent private values,is nonparametrically identified even if only the transaction price from each auction is observed. For richer models, identification and testable restrictions may be obtained when additional information of one or more of the following types is available: (i) the identity of the winning bidder or other bidders; (ii) one or more bids in addition to the transaction price; (iii) exogenous variation in the number of bidders; (iv) bidder,specific covariates. While many private values (PV) models are nonparametrically identified and testable with commonly available data, identification of common values (CV) models requires stringent assumptions. Nonetheless, the PV model can be tested against the CV alternative, even when neither model is identified. [source]

On the use of the intensity-scale verification technique to assess operational precipitation forecasts

METEOROLOGICAL APPLICATIONS, Issue 1 2008
Gabriella Csima
Abstract The article describes the attempt to include the intensity-scale technique introduced by Casati et al. (2004) into a set of standardized verifications used in operational centres. The intensity-scale verification approach accounts for the spatial structure of the forecast field and allows the skill to be diagnosed as a function of the scale of the forecast error and intensity of the precipitation events. The intensity-scale method has been used to verify two different resolutions of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational quantitative precipitation forecast (QPF) over France, and to compare the performance of the ECMWF and the Hungarian Meteorological Service operational model (ALADIN) forecasts, run over Hungary. Two case studies have been introduced, which show some interesting insight into the spatial scale of the error. The distribution of daily skill score for an extended period of time is also presented. The intensity-scale technique shows that the forecasts in general exhibit better skill for large-scale events, and lower skill for small-scale and intense events. In the paper, it is mentioned how some of the stringent assumptions on the domain over which the method can be applied, and the availability of the matched forecasts and observations, can limit its usability in an operational environment. Copyright © 2008 Royal Meteorological Society [source]

Smoothness adaptive average derivative estimation

THE ECONOMETRICS JOURNAL, Issue 1 2010
Marcia M. A. Schafgans
Summary, Many important models utilize estimation of average derivatives of the conditional mean function. Asymptotic results in the literature on density weighted average derivative estimators (ADE) focus on convergence at parametric rates; this requires making stringent assumptions on smoothness of the underlying density; here we derive asymptotic properties under relaxed smoothness assumptions. We adapt to the unknown smoothness in the model by consistently estimating the optimal bandwidth rate and using linear combinations of ADE estimators for different kernels and bandwidths. Linear combinations of estimators (i) can have smaller asymptotic mean squared error (AMSE) than an estimator with an optimal bandwidth and (ii) when based on estimated optimal rate bandwidth can adapt to unknown smoothness and achieve rate optimality. Our combined estimator minimizes the trace of estimated MSE of linear combinations. Monte Carlo results for ADE confirm good performance of the combined estimator. [source]