Kernel density

ABSTRACT Maize grain yields have increased dramatically over the past fifty years, and concurrently end-uses have proliferated requiring special quality characteristics. Plant breeders have developed many specialty types of maize, all of which are influenced by the agronomic practices used to produce the crop. Grain yield increases have resulted in lower protein concentration except when the yield increase resulted from nitrogen fertilizer application. Irrigation improves the biological value of protein, while higher nitrogen application rates alter the amino acid balance thereby reducing the nutritional value. Kernel breakage susceptibility and kernel density increase with higher nitrogen fertilizer application rates, and are reduced by irrigation. Extractable starch and oil concentration are largely influenced by hybrid choice, but small production practice effects have been documented. Essential mineral nutrient levels are often influenced by soil or foliar fertilizer application. The production of aflatoxin can be reduced by irrigation or other strategies to avoid water stress during grain fill. Although genetics usually exerts the largest effect on maize grain quality, agronomic practices are also important.

Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level understanding of events requires that certain lower level computer vision tasks be performed. These may include detection of unusual motion, tracking targets, labeling body parts, and understanding the interactions between people. To achieve many of these tasks, it is necessary to build representations of the appearance of objects in the scene. This paper focuses on two issues related to this problem. First, we ...

We analyze the redistributive role played by governments during the 1990s expansionary economic cycle in several OECD countries. We find a duality among countries: while governments in the Euro-area play a crucial role in the redistributive process, government interventions reduce the equalitarian effect of the market in the Anglo-Saxon economies.

We investigate multi-task learning from an output space regularization perspective. Most multi-task approaches tie together related tasks by constraining them to share input spaces and function classes. In contrast to this, we propose a multi-task paradigm which we call output space regularization, in which the only constraint is that the output spaces of the multiple tasks are related. We focus on a specic instance of output space regularization, multi-task averaging, that is both widely applicable and amenable to analysis. The multi-task averaging estimator improves on the single-task sample average under certain conditions, which we detail. Our analysis shows that for a simple case the optimal similarity depends on the ratio of the task variance to the task dierences, but that for more complicated cases the optimal similarity behaves nonlinearly. Further, we show that the estimates produced are a convex combination of the tasks’ sample averages. We discuss the Bayesian viewpoint....

Based on the nonlinear system theory, we introduce previously undescribed dependence measures for stationary causal processes. Our physical and predictive dependence measures quantify the degree of dependence of outputs on inputs in physical systems. The proposed dependence measures provide a natural framework for a limit theory for stationary processes. In particular, under conditions with quite simple forms, we present limit theorems for partial sums, empirical processes, and kernel density estimates. The conditions are mild and easily verifiable because they are directly related to the data-generating mechanisms.

La conoscenza della distribuzione spaziale e temporale dei punti di innesco degli incendi appare fondamentale per la stima del rischio. Per meglio integrare le informazioni di tipo areale, e opportuno convertire l’ubicazione, di tipo puntiforme, dei punti di innesco in variabili continue, esprimendo la densita in termini agevolmente interpretabili, per es. come numero di punti per km 2 . Il presente lavoro illustra una tecnica di rappresentazione della densita dei punti attraverso una variabile continua, mediante la tecnica dell’ adaptive kernel interpolation . L’area di studio, estesa circa 1500 km 2 e caratterizzata da un’elevata frequenza di incendi, e situata nel promontorio del Gargano. La densita dei punti di innesco e stata valutata considerando tutti gli incendi (583) verificatisi nel periodo 1997-2002. Il lavoro illustra anche le modalita di calibrazione necessarie per adattare il metodo alle condizioni locali. La mappa ottenuta ha consentito di analizzare la distribuzione ...

In this paper we develop a regression and a kernel density based model for finding fixed points and attractors of dynamical systems to explore attractors of structural change for NICs. The results show that countries consume longer time in some structures than the others. This can be interpreted as existence of attractors that pull countries to themselves in the first stage of the development. In the other words one attractor (low level attractor) prevent countries to reach industrial structure. Awareness of this can be helpful in policymaking for transition from one structure to another. This analysis shades light on the problem that 'why some countries can not get ride of traditional structure?' or bad structure phenomena.

A significant role for hypothesis testing in econometrics involves diagnostic checking. When checking the adequacy of a chosen model, researchers typically employ a range of diagnostic tests, each of which is designed to detect a particular form of model inadequacy. A major problem is how best to control the overall probability of rejecting the model when it is true and multiple test statistics are used. This paper presents a new multiple testing procedure, which involves checking whether the calculated values of the diagnostic statistics are consistent with the postulated model being true. This is done through a combination of bootstrapping to obtain a multivariate kernel density estimator of the joint density of the test statistics under the null hypothesis and Monte Carlo simulations to obtain a p value using this kernel density. We prove that under some regularity conditions, the estimated p value of our test procedure is a consistent estimate of the true p value. The proposed t...

Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM ...