9 resultados para function estimation
em University of Queensland eSpace - Australia
Resumo:
This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli ( 1992).
Resumo:
A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.
Resumo:
The relationship between reported treatments of lameness, metabolic disorders (milk fever, ketosis), digestive disorders, and technical efficiency (TE) was investigated using neutral and non-neutral stochastic frontier analysis (SFA). TE is estimated relative to the stochastic frontier production function for a sample of 574 Danish dairy herds collected in 1997. Contrary to most published results, but in line with the expected negative impact of disorders on the average cow milk production, herds reporting higher frequencies of milk fever are less technically efficient. Unexpectedly, however, the opposite results were observed for lameness, ketosis, and digestive disorders. The non-neutral stochastic frontier indicated that the opposite results are due to the relative. high productivities of inputs. The productivity of the cows is also reflected by the direction of impact of herd management variables. Whereas efficient farms replace cows more frequently, enroll heifers in production at an earlier age, and have shorter calving intervals, they also report higher frequency of disorder treatments. The average estimated energy corrected milk loss per cow is 1036, 451 and 242 kg for low, medium and high efficient farms. The study demonstrates the benefit of the stochastic frontier production function involving the estimation of individual technical efficiencies to evaluate farm performance and investigate the source of inefficiency. (C) 2004 Elsevier B.V. All rights reserved.
Resumo:
In this paper we investigate a Bayesian procedure for the estimation of a flexible generalised distribution, notably the MacGillivray adaptation of the g-and-κ distribution. This distribution, described through its inverse cdf or quantile function, generalises the standard normal through extra parameters which together describe skewness and kurtosis. The standard quantile-based methods for estimating the parameters of generalised distributions are often arbitrary and do not rely on computation of the likelihood. MCMC, however, provides a simulation-based alternative for obtaining the maximum likelihood estimates of parameters of these distributions or for deriving posterior estimates of the parameters through a Bayesian framework. In this paper we adopt the latter approach, The proposed methodology is illustrated through an application in which the parameter of interest is slightly skewed.
Resumo:
The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within- family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.
Resumo:
Background Nurses play a key role in the prevention of cardiovascular disease (CVD) and one would, therefore, expect them to have a heightened awareness of the need for systematic screening and their own CVD risk profile. The aim of this study was to examine personal awareness of CVD risk among a cohort of cardiovascular nurses attending a European conference. Methods Of the 340 delegates attending the 5th annual Spring Meeting on Cardiovascular Nursing (Basel, Switzerland, 2005), 287 (83%) completed a self-report questionnaire to assess their own risk factors for CVD. Delegates were also asked to give an estimation of their absolute total risk of experiencing a fatal CVD event in the next 10 years. Level of agreement between self-reported CVD risk estimation and their actual risk according to the SCORE risk assessment system was compared by calculating weighted Kappa (κw). Results Overall, 109 responders (38%) self-reported having either pre-existing CVD (only 2%), one or more markedly raised CVD risk factors, a high total risk of fatal CVD (≥ 5% in 10 years) or a strong family history of CVD. About half of this cohort (53%) did not know their own total cholesterol level. Less than half (45%) reported having a 10-year risk of fatal CVD of < 1%, while 13% reported having a risk ≥ 5%. Based on the SCORE risk function, the estimated 10-year risk of a fatal CVD event was < 1% for 96% of responders: only 2% had a ≥ 5% risk of such an event. Overall, less than half (46%) of this cohort's self-reported CVD risk corresponded with that calculated using the SCORE risk function (κw = 0.27). Conclusion Most cardiovascular nurses attending a European conference in 2005 poorly understood their own CVD risk profile, and the agreement between their self-reported 10-year risk of a fatal CVD and their CVD risk using SCORE was only fair. Given the specialist nature of this conference, our findings clearly demonstrate a need to improve overall nursing awareness of the role and importance of systematic CVD risk assessment.
Resumo:
The estimation of a concentration-dependent diffusion coefficient in a drying process is known as an inverse coefficient problem. The solution is sought wherein the space-average concentration is known as function of time (mass loss monitoring). The problem is stated as the minimization of a functional and gradient-based algorithms are used to solve it. Many numerical and experimental examples that demonstrate the effectiveness of the proposed approach are presented. Thin slab drying was carried out in an isothermal drying chamber built in our laboratory. The diffusion coefficients of fructose obtained with the present method are compared with existing literature results.