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Elicitation and evaluation of statistical forecasts

by Nicolas S. Lambert , 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 ..."
Abstract - Cited by 9 (0 self) - Add to MetaCart
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 quasi-geostrophic model using Monte Carlo methods to forecast error statistics

by Geir Evensen - 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 ..."
Abstract - Cited by 800 (23 self) - Add to MetaCart
. 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

EXTRAPOLATIVE-STATISTICAL FORECASTS OF RADAR REFLECTIVITY

by David H. Kitzmiller, Jeffrey B. Ator
"... A common approach to short-range precipitation forecast-ing 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-l ..."
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A common approach to short-range precipitation forecast-ing 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

Large-scale parallel statistical forecasting computations in R

by Murray Stokely, Farzan Rohani, Eric Tassone - 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 high-level language that are then broken up and distributed to worker tasks in Google datacenters. Result ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
. 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

by P. L. Houtekamer, Herschel L. Mitchell , 1998
"... The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated ob ..."
Abstract - Cited by 423 (5 self) - Add to MetaCart
The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated

Macroeconomic Forecasting Using Diffusion Indexes

by James H. Stock, Mark W. Watson - 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 ..."
Abstract - Cited by 326 (4 self) - Add to MetaCart
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

by Tyler Shumway - 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 single-period models used previously. Single-period bankruptcy models give biased and inconsistent probability estimates while hazard models produce consistent estimates. I describe a simple technique for estimating ..."
Abstract - Cited by 358 (1 self) - Add to MetaCart
I argue that hazard models are more appropriate for forecasting bankruptcy than the single-period models used previously. Single-period 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

by Rich Wolski , 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 end-to-end throughput and latency that is attainable by an application using systems located at different ..."
Abstract - Cited by 291 (37 self) - Add to MetaCart
sites. Such network forecasts are needed both to support scheduling [5], and by the metacomputing software infrastructure to develop quality-of-service guarantees [10, 17]. Keywords: scheduling, metacomputing, quality-of-service, statistical forecasting, network performance monitoring

FORECAST COMBINATION AND THE BANK OF ENGLAND’S SUITE OF STATISTICAL FORECASTING MODELS

by Paul Goodwin, George Kapetanios, His Colleagues (kapetanios
"... 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

by Michael Schirmer, Michael Lehning - 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 snow-stratigraphy data. Detailed snow-cover data were rarely used because they were not readil ..."
Abstract - Cited by 9 (2 self) - Add to MetaCart
important simulated snow variables. Data mining and statistical methods, including classification trees, artificial neural networks, support vector machines, hidden Markov models and nearest-neighbour methods were trained on the forecasted regional avalanche danger (European avalanche danger scale
Results 1 - 10 of 3,848