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Random Term (random + term)
Selected AbstractsCross Section and Panel Data Estimators for Nonseparable Models with Endogenous RegressorsECONOMETRICA, Issue 4 2005Joseph G. Altonji We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group. [source] Nonparametric Estimation of Nonadditive Random FunctionsECONOMETRICA, Issue 5 2003Rosa L. Matzkin We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented. [source] Habitat models of bird species' distribution: an aid to the management of coastal grazing marshesJOURNAL OF APPLIED ECOLOGY, Issue 5 2000T. P. Milsom 1.,Coastal grazing marshes comprise an important habitat for wetland biota but are threatened by agricultural intensification and conversion to arable farmland. In Britain, the Environmentally Sensitive Area (ESA) scheme addresses these problems by providing financial incentives to farmers to retain their grazing marshes, and to follow conservation management prescriptions. 2.,A modelling approach was used to aid the development of management prescriptions for ground-nesting birds in the North Kent Marshes ESA. This ESA contains the largest area of coastal grazing marsh remaining in England and Wales (c. 6500 ha) and supports nationally important breeding populations of lapwing Vanellus vanellus and redshank Tringa totanus. 3.,Counts of ground-nesting birds, and assessments of sward structure, surface topography and wetness, landscape structure and sources of human disturbance were made in 1995 and again in 1996, on 19 land-holdings with a combined area of c. 3000 ha. The land-holdings varied from nature reserves at one extreme to an intensive dairy farm at the other. 4.,Models of relationship between the presence or absence of ground-nesting birds and the grazing marsh habitat in each of c. 430 marshes were constructed using a generalized linear mixed modelling (GLMM) method. This is an extension to the conventional logistic regression approach, in which a random term is used to model differences in the proportion of marshes occupied on different land-holdings. 5.,The combined species models predicted that the probability of marshes being occupied by at least one ground-nesting species increased concomitantly with the complexity of the grass sward and surface topography but decreased in the presence of hedgerows, roads and power lines. 6.,Models were also prepared for each of the 10 most widespread species, including lapwing and redshank. Their composition differed between species. Variables describing the sward were included in models for five species: heterogeneity of sward height tended to be more important than mean sward height. Surface topography and wetness were important for waders and wildfowl but not for other species. Effects of boundaries, proximity to roads and power lines were included in some models and were negative in all cases. 7.,Binomial GLMMs are useful for investigating habitat factors that affect the distribution of birds at two nested spatial scales, in this case fields (marshes) grouped within farms. Models of the type presented in this paper provide a framework for targeting of conservation management prescriptions for ground-nesting birds at the field scale on the North Kent Marshes ESA and on lowland wet grassland elsewhere in Europe. [source] The importance of weather and agronomic factors for the overwinter survival of yellow rust (Puccinia striiformis) and subsequent disease risk in commercial wheat crops in EnglandANNALS OF APPLIED BIOLOGY, Issue 3 2007P. Gladders Abstract Disease survey data from 4475 randomly selected crops of wheat from England and Wales during 1985,2000 showed that yellow rust was most prevalent in 1988, 1989, 1990, 1998 and 1999. Disease severity on the upper two leaves was low as >95% crops had received foliar fungicides. Factors affecting the presence or absence (incidence) of yellow rust were investigated using random effects logistic regression (general linear mixed model). This enabled crop management (risk) variables for individual crops to be combined with meteorological variables measured at the county level. Two models are presented that analysed the effect of host genotype on incidence either solely through yellow rust resistance rating (Model 1) or by including both resistance rating (fixed effect) and cultivar (fitted as a random term) (Model 2). In both models, the percentage of crops with yellow rust decreased with cultivar disease resistance ratings ,3, the occurrence of severe frosts (<,5°C), use of systemic seed treatment and application of foliar fungicide sprays. There were no significant effects (P < 0.05) of timing of fungicide sprays, previous cropping or summer weather. The use of risk variables associated with overwintering survival may help adjust fungicide inputs to seasonal risk. [source] The Structural Error-in-Equation Model to Evaluate Individual BioequivalenceBIOMETRICAL JOURNAL, Issue 5 2005Josep L. Carrasco Abstract Individual bioequivalence is assessed using an extension of the classical structural equation model, known as the error-in-equation model. This procedure estimates the relationship between individual means, as well as the variance-covariance parameters, of the bioavailabilities measurement model, by considering individual means related through a straight line with a random term, whereas the classical structural equation considers a deterministic linear relationship. We discuss the implications of this approach in terms of the bioavailabilities measurement model and how to test the overall hypothesis of individual bioequivalence. Both models are compared in a simulation study and a case example is presented. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Pharmacokinetic assessment of a five-probe cocktail for CYPs 1A2, 2C9, 2C19, 2D6 and 3ABRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 6 2009Sandrine Turpault WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT , Numerous cocktails using concurrent administration of several cytochrome P450 (CYP) isoform-selective probe drugs have been reported to investigate drug,drug interactions in vivo. , This approach has several advantages: characterize the inhibitory or induction potential of compounds in development toward the CYP enzymes identified in vitro in an in vivo situation, assess several enzymes in the same trial, and have complete in vivo information about potential CYP-based drug interactions. WHAT THIS STUDY ADDS , This study describes a new cocktail containing five probe drugs that has never been published. , This cocktail can be used to test the effects of a new chemical entity on multiple CYP isoforms in a single clinical study: CYP1A2 (caffeine), CYP2C9 (warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol), and CYP3A (midazolam) and was designed to overcome potential liabilities of other reported cocktails. AIMS To assess the pharmacokinetics (PK) of selective substrates of CYP1A2 (caffeine), CYP2C9 (S-warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol) and CYP3A (midazolam) when administered orally and concurrently as a cocktail relative to the drugs administered alone. METHODS This was an open-label, single-dose, randomized, six-treatment six-period six-sequence William's design study with a wash-out of 7 or 14 days. Thirty healthy male subjects received 100 mg caffeine, 100 mg metoprolol, 0.03 mg kg,1 midazolam, 20 mg omeprazole and 10 mg warfarin individually and in combination (cocktail). Poor metabolizers of CYP2C9, 2C19 and 2D6 were excluded. Plasma samples were obtained up to 48 h for caffeine, metoprolol and omeprazole, 12 h for midazolam, 312 h for warfarin and the cocktail. Three different validated liquid chromatography tandem mass spectrometry methods were used. Noncompartmental PK parameters were calculated. Log-transformed Cmax, AUClast and AUC for each analyte were analysed with a linear mixed effects model with fixed term for treatment, sequence and period, and random term for subject within sequence. Point estimates (90% CI) for treatment ratios (individual/cocktail) were computed for each analyte Cmax, AUClast and AUC. RESULTS There was no PK interaction between the probe drugs when administered in combination as a cocktail, relative to the probes administered alone, as the 90% CI of the PK parameters was within the prespecified bioequivalence limits of 0.80, 1.25. CONCLUSION The lack of interaction between probes indicates that this cocktail could be used to evaluate the potential for multiple drug,drug interactions in vivo. [source] Identification in Nonparametric Simultaneous Equations ModelsECONOMETRICA, Issue 5 2008Rosa L. Matzkin This paper provides conditions for identification of functionals in nonparametric simultaneous equations models with nonadditive unobservable random terms. The conditions are derived from a characterization of observational equivalence between models. We show that, in the models considered, observational equivalence can be characterized by a restriction on the rank of a matrix. The use of the new results is exemplified by deriving previously known results about identification in parametric and nonparametric models as well as new results. A stylized method for analyzing identification, which is useful in some situations, is also presented. [source] Nonparametric Estimation of Nonadditive Random FunctionsECONOMETRICA, Issue 5 2003Rosa L. Matzkin We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented. [source] | ||||||||||