Unbiased Predictors (unbiased + predictor)

Distribution by Scientific Domains

Selected Abstracts

Fixed rank kriging for very large spatial data sets

Noel Cressie
Summary., Spatial statistics for very large spatial data sets is challenging. The size of the data set, n, causes problems in computing optimal spatial predictors such as kriging, since its computational cost is of order . In addition, a large data set is often defined on a large spatial domain, so the spatial process of interest typically exhibits non-stationary behaviour over that domain. A flexible family of non-stationary covariance functions is defined by using a set of basis functions that is fixed in number, which leads to a spatial prediction method that we call fixed rank kriging. Specifically, fixed rank kriging is kriging within this class of non-stationary covariance functions. It relies on computational simplifications when n is very large, for obtaining the spatial best linear unbiased predictor and its mean-squared prediction error for a hidden spatial process. A method based on minimizing a weighted Frobenius norm yields best estimators of the covariance function parameters, which are then substituted into the fixed rank kriging equations. The new methodology is applied to a very large data set of total column ozone data, observed over the entire globe, where n is of the order of hundreds of thousands. [source]

An insertion/deletion variant of a thymine base in exon 2 of the porcine beta 3-adrenergic receptor gene associated with loin eye muscle area

Kensuke HIROSE
ABSTRACT An insertion/deletion variant of a thymine base (T5 and T6) in exon 2 of porcine beta 3-adrenergic receptor (ADRB3) gene has been described. In the current study, we made an association study between the ADRB3 polymorphisms and production traits in 735 Duroc pigs. The allele frequencies for the T5 and T6 alleles in our study population were 0.433 and 0.567, respectively. Any associations between ADRB3 genotype and average daily weight gain during test period, or backfat thickness and intramuscular fat content were not detected in either sex. However the size of the loin eye muscle area (EMA) was significantly associated with ADRB3 genotypes in gilts. T6-homozygous gilts had a higher mean of EMA (40.6 ± 0.6 cm2) than T5-homozygous (38.1 ± 0.4 cm2, P = 0.002) and heterozygous (38.8 ± 0.3 cm2, P = 0.034) gilts. This association was not detected in males. In addition, a multiple traits animal model best linear unbiased predictor (BLUP) analysis revealed that the T6-homozygous genotype had positive effects on breeding value of EMA. Accordingly, we suggest that ADRB3 polymorphism has the potential to be an important genetic marker for prediction of EMA in Duroc pigs. [source]

An explanation of the forward premium ,puzzle'

Richard Roll
Existing literature reports a puzzle about the forward rate premium over the spot foreign exchange rate. The premium is often negatively correlated with subsequent changes in the spot rate. This defies economic intuition and possibly violates market efficiency. Rational explanations include non-stationary risk premia and econometric mis-specifications, but some embrace the puzzle as a guide to profitable trading. We suggest there is really no puzzle. A simple model fits the data: forward exchange rates are unbiased predictors of subsequent spot rates. The puzzle arises because the forward rate, the spot rate, and the forward premium follow nearly non-stationary time series processes. We document these properties with an extended sample and show why they give the delusion of a puzzle. [source]

The Kalman filter for the pedologist's tool kit

R. Webster
Summary The Kalman filter is a tool designed primarily to estimate the values of the ,state' of a dynamic system in time. There are two main equations. These are the state equation, which describes the behaviour of the state over time, and the measurement equation, which describes at what times and in what manner the state is observed. For the discrete Kalman filter, discussed in this paper, the state equation is a stochastic difference equation that incorporates a random component for noise in the system and that may include external forcing. The measurement equation is defined such that it can handle indirect measurements, gaps in the sequence of measurements and measurement errors. The Kalman filter operates recursively to predict forwards one step at a time the state of the system from the previously predicted state and the next measurement. Its predictions are optimal in the sense that they have minimum variance among all unbiased predictors, and in this respect the filter behaves like kriging. The equations can also be applied in reverse order to estimate the state variable at all time points from a complete series of measurements, including past, present and future measurements. This process is known as smoothing. This paper describes the ,predictor,corrector' algorithm for the Kalman filter and smoother with all the equations in full, and it illustrates the method with examples on the dynamics of groundwater level in the soil. The height of the water table at any one time depends partly on the height at previous times and partly on the precipitation excess. Measurements of the height of water table and their errors are incorporated into the measurement equation to improve prediction. Results show how diminishing the measurement error increases the accuracy of the predictions, and estimates achieved with the Kalman smoother are even more accurate. Le filtre de Kalman comme outil pour le pédologue Résumé Le filtre de Kalman est un outil conçu essentiellement pour estimer les valeurs de l'état d'un système dynamique dans le temps. Il comprend deux équations principales. Celles-ci sont l'équation d'état, qui décrit l'évolution de l'état pendant le temps, et l'équation de mesure qui decrit à quel instants et de quelle façon on observe l'état. Pour le filtre discret de Kalman, décrit dans cet article, l'équation d'état est une équation stochastique différentielle qui comprend une composante aléatoire pour le bruit dans le système et qui peut inclure une force extérieure. On définit l'équation de mesure de façon à ce qu'elle puisse traiter des mesures indirectes, des vides dans des séquences de mesures et des erreurs de mesure. Le filtre de Kalman fonctionne récursivement pour prédire en avance une démarche à temps l'état du système de la démarche prédite antérieure plus l'observation prochaine. Ses prédictions sont optimales dans le sens qu'elles minimisent la variance parmi toutes les prédictions non-biasées, et à cet égard le filtre se comporte comme le krigeage. On peut appliquer, aussi, les équations dans l'ordre inverse pour estimer la variable d'état à toutes pointes à toutes les instants d'une série complète d'observations, y compris les observations du passé, du présent et du futur. Ce processus est connu comme ,smoothing'. Cet article décrit l'algorithme ,predictor,corrector' du filtre de Kalman et le ,smoother' avec toutes les équations entières. Il illustre cette méthode avec des exemples de la dynamique du niveau de la nappe phréatique dans le sol. Le niveau de la nappe à un instant particulier dépend en partie du niveau aux instants précédents et en partie de l'excès de la précipitation. L'équation d'état fournit la relation générale entre les deux variables et les prédictions. On incorpore les mesures du niveau de la nappe et leurs erreurs pour améliorer les prédictions. Les résultats mettent en évidence que lorsqu'on diminue l'erreur de mesure la précision des prédictions augmente, et aussi que les estimations avec le ,smoother' de Kalman sont encore plus précises. [source]

Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets

Lorne N. Switzer
This study investigates the efficiency of the New York Mercantile Exchange (NYMEX) Division light sweet crude oil futures contract market during recent periods of extreme conditional volatility. Crude oil futures contract prices are found to be cointegrated with spot prices and unbiased predictors of future spot prices, including the period prior to the onset of the Iraqi war and until the formation of the new Iraqi government in April 2005. Both futures and spot prices exhibit asymmetric volatility characteristics. Hedging performance is improved when asymmetries are accounted for. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:61,84, 2007 [source]