44 research outputs found
Dynamical seasonal prediction of southern African summer precipitation
Prediction skill for southern African (16 –
33 E, 22 –35 S) summer precipitation in the Scale Interaction
Experiment-Frontier coupled model is assessed for
the period of 1982–2008. Using three different observation
datasets, deterministic forecasts are evaluated by anomaly
correlation coefficients, whereas scores of relative operating
characteristic and relative operating level are used to
evaluate probabilistic forecasts. We have found that these
scores for December–February precipitation forecasts initialized
on October 1st are significant at 95 % confidence
level. On a local scale, the level of prediction skill in the
northwestern and central parts of southern Africa is higher
than that in northeastern South Africa. El Nin˜o/Southern
Oscillation (ENSO) provides the major source of predictability,
but the relationship with ENSO is too strong in the
model. The Benguela Nin˜o, the basin mode in the tropical
Indian Ocean, the subtropical dipole modes in the South
Atlantic and the southern Indian Oceans and ENSO Modoki
may provide additional sources of predictability.
Within the wet season from October to the following April,
the precipitation anomalies in December-February are the most predictable. This study presents promising results for
seasonal prediction of precipitation anomaly in the extratropics,
where seasonal prediction has been considered a
difficult task.Japan Science and Technology Agency (JST) and Japan International Cooperation Agency (JICA) through Science and Technology Research Partnership for Sustainable Development (SATREPS).http://link.springer.com/journal/382hb201
Impacts of IOD, ENSO and ENSO Modoki on the Australian Winter Wheat Yields in Recent Decades
The Other Coastal Nino/Nina - The Benguela, California and Dakar Ninos/Ninas in TROPICAL AND EXTRA-TROPICAL AIR-SEA INTERACTIONS, edited by Swadhin Behera
Model averaging for generalized linear models in fragmentary data prediction
Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response variable is discrete. In this paper, we propose a model averaging method for generalized linear models in fragmentary data prediction. The candidate models are fitted based on different combinations of covariate availability and sample size. The optimal weight is selected by minimizing the Kullback–Leibler loss in the completed cases and its asymptotic optimality is established. Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented
Model averaging for generalized linear models in diverging model spaces with effective model size
Econometric Reviews430171-9
Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces
Support vector machine (SVM) is a powerful classification method that has
achieved great success in many fields. Since its performance can be seriously
impaired by redundant covariates, model selection techniques are widely used
for SVM with high dimensional covariates. As an alternative to model selection,
significant progress has been made in the area of model averaging in the past
decades. Yet no frequentist model averaging method was considered for SVM. This
work aims to fill the gap and to propose a frequentist model averaging
procedure for SVM which selects the optimal weight by cross validation. Even
when the number of covariates diverges at an exponential rate of the sample
size, we show asymptotic optimality of the proposed method in the sense that
the ratio of its hinge loss to the lowest possible loss converges to one. We
also derive the convergence rate which provides more insights to model
averaging. Compared to model selection methods of SVM which require a tedious
but critical task of tuning parameter selection, the model averaging method
avoids the task and shows promising performances in the empirical studies.Comment: need to be improved furthe