NONPARAMETRIC KERNEL DISTRIBUTION FUNCTION ESTIMATION NEAR ENDPOINTS

In this paper, two kernel cumulative distribution function estimators are introduced and investigated in order to improve the boundary effects, we will restrict our attention to the right boundary. The first estimator uses a self-elimination between modify theoretical Bias term and the classical kernel estimator itself. The basic technique of construction the second estimator is kind of a generalized reflection method involving reflection a transformation of the observed data. The theoretical properties of our estimators turned out that the Bias has been reduced to the second power of the bandwidth, simulation studies and two real data applications were carried out to check these phenomena and are conducted that the proposed estimators are better than the existing boundary correction methods.

The Generalised Plug-in Algorithm for the Diffeomorphism Kernel Estimate

The optimal value of the smoothing parameter of the Kernel estimator can be obtained by the well known Plug-in algorithm. The optimality is realised in the sense of Mean Integrated Square Error (MISE). In this paper, we propose to generalise this algorithm to the case of the difficult problem of the estimation of a distribution which has a bounded support. The proposed algorithm consists in searching the optimal smoothing parameter by iterations from the expression of MISE of the kernel-diffeomorphism estimator. By some simulations applied to some distribution having a support bounded and semi bounded, we show that the support of the pdf estimator respects the one of the theoretical distribution. We also prove that the proposed method minimizes the Gibbs phenomenon.

A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression

Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson kernel estimator (NWK) is one of the most important nonparametric kernel estimator that is often used in regression models with a fixed bandwidth. In this article, we consider the four new Proposed Adaptive Nadaraya-Watson Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Absolute Deviation) rather than (Fixed Bandwidth, Adaptive Geometric, Adaptive Mean, Adaptive Range, and Adaptive Median). The outcomes in both simulation and actual data in Leukemia Cancer show that the four new ANW Kernel Estimators (Interquartile Range, Standard Deviation, Mean Absolute devotion, and Median Absolute Deviation) is more effective than the kernel estimations with fixed bandwidth in previous studies using Mean Square Error (MSE) Criterion.

Clustering using kernel entropy principal component analysis and variable kernel estimator

Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the kernel function into the EPCA, the input space can be mapped implicitly into a high-dimensional of feature space. Then, the Shannon’s entropy estimated via the inertia provided by the contribution of every mapped object in data is the key measure to determine the optimal extracted features space. Our proposed method performs very well the clustering algorithm of the fast search of clusters’ centers based on the local densities’ computing. Experimental results disclose that the approach is feasible and efficient on the performance query.

Dinâmica de Queimadas no Município de Cuiabá-MT por Sensoriamento Remoto

O bioma Cerrado é uma das mais ricas fitofisionomias existentes no Planeta com destaque no elevado índice de ocupação humana direcionada à produção agropecuária. Apesar de seu potencial biológico enfrenta ameaças constantes de queimadas devido à conversão da vegetação em parcelas destinada a agricultura e pastagens. Neste contexto o objetivo deste trabalho é estudar a dinâmica espacial e temporal das queimadas no município de Cuiabá-MT, relacionando com as variáveis microclimáticas, classes de vegetação e declividades do terreno com o uso de recursos, de sensoriamento remoto. Foram utilizados os índices espectrais, NBR, NBR2 e NDVI extraídos das imagens Landsat 8 e focos de calor fornecido pelo Banco de Dados de Queimadas do INPE (Instituto Nacional de Pesquisas Espaciais) no período de 2013 a 2017. Os índices espectrais foram extraídos de 25 imagens referente a órbita 226 e ponto 071, utilizando o programa Erdas Imagine e os mapas de fogo através do estimador de Kernel presente no ArcGis 10.3 a fim de avaliar a distribuição e o padrão das queimadas na área proposta. Os resultados avaliados a partir do conjunto dos índices espectrais e dos mapas de estimativa de Kernel mostraram que o município de Cuiabá apresentou um padrão sazonal de queimadas, evidenciando maiores volumes de queimadas nas formações savânicas e nos terrenos de declividades da classe suave-ondulado em todo o período estudado.Palavras-chave: OLI, precipitação, padrão espacial. Dynamics of Fires in the Municipality of Cuiabá-MT by Remote Sensing A B S T R A C T The Brazilian Cerrado biome has several phytophysiognomies and a high rate of human occupation, with emphasis on agricultural production. Despite its biological potential, it faces constant threats of burning due to the conversion of vegetation into plots for agriculture and pasture. In this context, the objective of this work was to identify and relate the spatial and temporal dynamics of the fires in the municipality of Cuiabá-MT, with the microclimate variables, classes of vegetation and slopes of the land through the use of remote sensing resources. The spectral indexes NBR (Normalized Burn Ratio, NBR2 (Variation of Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) extracted from Landsat 8 images and heat sources provided by the INPE (Instituto Nacional de Space Research) from 2013 to 2017. The spectral indexes were extracted from 25 images referring to orbit 226 and point 071, using the Erdas Imagine program, and the fire maps of the Kernel estimator present in ArcGis 10.3 in order to evaluate the distribution and the pattern of fires in the proposed area. There was a 50.68% coincidence of the total number of hot spots on the reference burned areas, with a higher percentage of 72.12% in 2017 and lower in 2014 of 12.95%. These results made it possible to elaborate maps with a characteristic burning pattern and to highlight the classes most affected by fire throughout the studied period. Keywords: OLI, fire, spatial pattern.

A New Kernel Estimator of Copulas Based on Beta Quantile Transformations

A copula is a multivariate cumulative distribution function with marginal distributions Uniform(0,1). For this reason, a classical kernel estimator does not work and this estimator needs to be corrected at boundaries, which increases the difficulty of the estimation and, in practice, the bias boundary correction might not provide the desired improvement. A quantile transformation of marginals is a way to improve the classical kernel approach. This paper shows a Beta quantile transformation to be optimal and analyses a kernel estimator based on this transformation. Furthermore, the basic properties that allow the new estimator to be used for inference on extreme value copulas are tested. The results of a simulation study show how the new nonparametric estimator improves alternative kernel estimators of copulas. We illustrate our proposal with a financial risk data analysis.

Modeling elderly behavioral patterns in single-person households

This paper proposes and describes an unsupervised computational model that monitors an elderly person who lives alone and issues alarms when a risk to the elderly person’s well-being is identified. This model is based on data extracted exclusively from passive infrared motion sensors connected to a ZigBee wireless network. The proposed monitoring system and model is non-intrusive, does not capture any images, and does not require any interaction with the monitored person. Thus, it is more likely to be adopted by members of the elderly population who might reject other more intrusive or complex types of technology. The developed computational model for activity discovery employs a kernel estimator and local outlier factor calculation, which are reliable and have a low computational cost. This model was tested with data collected over a period of 25 days from two elderly volunteers who live alone and have fairly different routines. The results demonstrate the model’s ability to learn relevant behaviors, as well as identify and issue alarms for atypical activities that can be suggestive of health problems. This low-cost, minimalistic sensor network approach is especially suited to the reality of underdeveloped (and developing) countries where assisted living communities are not available and low cost and ease of use are paramount.