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Elicitation and evaluation of statistical forecasts
, 2010
"... This paper studies mechanisms for eliciting and evaluating statistical forecasts. Nature draws a state at random from a given state space, according to some distribution p. Prior to Nature’s move, a forecaster, who knows p, provides a prediction for a given statistic of p. The mechanism defines the ..."
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Cited by 9 (0 self)
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This paper studies mechanisms for eliciting and evaluating statistical forecasts. Nature draws a state at random from a given state space, according to some distribution p. Prior to Nature’s move, a forecaster, who knows p, provides a prediction for a given statistic of p. The mechanism defines
Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
 J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
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Cited by 800 (23 self)
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. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
EXTRAPOLATIVESTATISTICAL FORECASTS OF RADAR REFLECTIVITY
"... A common approach to shortrange precipitation forecasting involves the extrapolation of radar reflectivity fields that ha ve been analyzed in digital form on a map grid. In an attempt to. refi.ne this basic technique, a large number of extrapolatlveforecasts were prepared and statistically correl ..."
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A common approach to shortrange precipitation forecasting involves the extrapolation of radar reflectivity fields that ha ve been analyzed in digital form on a map grid. In an attempt to. refi.ne this basic technique, a large number of extrapolatlveforecasts were prepared and statistically corre
Largescale parallel statistical forecasting computations in R
 In JSM Proceedings, Section on Physical and Engineering Sciences
, 2011
"... We demonstrate the utility of massively parallel computational infrastructure for statistical computing using the MapReduce paradigm for R. This framework allows users to write computations in a highlevel language that are then broken up and distributed to worker tasks in Google datacenters. Result ..."
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Cited by 4 (1 self)
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. Results are collected in a scalable, distributed data store and returned to the interactive user session. We apply our approach to a forecasting application that fits a variety of models, prohibiting an analytical description of the statistical uncertainty associated with the overall forecast. To overcome
Data Assimilation Using an Ensemble Kalman Filter Technique
, 1998
"... The possibility of performing data assimilation using the flowdependent statistics calculated from an ensemble of shortrange forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a threelevel, quasigeostrophic, T21 model and simulated ob ..."
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Cited by 423 (5 self)
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The possibility of performing data assimilation using the flowdependent statistics calculated from an ensemble of shortrange forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a threelevel, quasigeostrophic, T21 model and simulated
Macroeconomic Forecasting Using Diffusion Indexes
 Journal of Business and Economic Statistics
, 2002
"... This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimat ..."
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Cited by 326 (4 self)
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This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework
Forecasting bankruptcy more accurately: a simple hazard model
 0 otherwise P (Yit = 1) = FLOGIT (z 0 (i;t) ) with Yit = 1 , Y it < 0 where Y it = c + Z 0 (i;t) + " (i;t) and the
, 2001
"... I argue that hazard models are more appropriate for forecasting bankruptcy than the singleperiod models used previously. Singleperiod bankruptcy models give biased and inconsistent probability estimates while hazard models produce consistent estimates. I describe a simple technique for estimating ..."
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Cited by 358 (1 self)
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I argue that hazard models are more appropriate for forecasting bankruptcy than the singleperiod models used previously. Singleperiod bankruptcy models give biased and inconsistent probability estimates while hazard models produce consistent estimates. I describe a simple technique for estimating
Dynamically Forecasting Network Performance Using the Network Weather Service
, 1998
"... this paper, we outline its design and detail the predictive performance of the forecasts it generates. While the forecasting methods are general, we focus on their ability to predict the TCP/IP endtoend throughput and latency that is attainable by an application using systems located at different ..."
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Cited by 291 (37 self)
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sites. Such network forecasts are needed both to support scheduling [5], and by the metacomputing software infrastructure to develop qualityofservice guarantees [10, 17]. Keywords: scheduling, metacomputing, qualityofservice, statistical forecasting, network performance monitoring
FORECAST COMBINATION AND THE BANK OF ENGLAND’S SUITE OF STATISTICAL FORECASTING MODELS
"... One of the major findings of forecasting research over the last quarter century has been that greater predictive accuracy can often be achieved by combining forecasts from different methods or sources. Combination can be a process as straightforward as taking a simple average of the different foreca ..."
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One of the major findings of forecasting research over the last quarter century has been that greater predictive accuracy can often be achieved by combining forecasts from different methods or sources. Combination can be a process as straightforward as taking a simple average of the different
Statistical forecasting of regional avalanche danger using simulated snow cover data
 J. Glaciol
, 2009
"... ABSTRACT. In the past, numerical prediction of regional avalanche danger using statistical methods with meteorological input variables has shown insufficiently accurate results, possibly due to the lack of snowstratigraphy data. Detailed snowcover data were rarely used because they were not readil ..."
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Cited by 9 (2 self)
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important simulated snow variables. Data mining and statistical methods, including classification trees, artificial neural networks, support vector machines, hidden Markov models and nearestneighbour methods were trained on the forecasted regional avalanche danger (European avalanche danger scale
Results 1  10
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3,848