Search results for "Nonparametric"
showing 10 items of 427 documents
The impact of sample reduction on PCA-based feature extraction for supervised learning
2006
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…
Response Determination Criteria for ELISPOT: Toward a Standard that Can Be Applied Across Laboratories
2011
ELISPOT assay readout is often dichomized as positive or negative responses according to prespecified criteria. However, these criteria can vary widely across institutions. The adoption of a common response criterion is a key step toward cross-laboratory comparability. This chapter describes the two main approaches to response determination, identifying the strengths and limitations of each. Nonparametric statistical tests and consideration of data quality are recommended and instructions provided for their ready implementation by nonstatisticians and statisticians alike.
Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection
2012
Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time-frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time-frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS…
Automatic regrouping of strata in the goodness-of-fit chi-square test
2019
Pearson’s chi-square test is widely employed in social and health sciences to analyze categorical data and contingency tables. For the test to be valid, the sample size must be large enough to provide a minimum number of expected elements per category. This paper develops functions for regrouping strata automatically no matter where they are located, thus enabling the goodness-of-fit test to be performed within an iterative procedure. The functions are written in Excel VBA (Visual Basic for Applications) and in Mathematica. The usefulness and performance of these functions is illustrated by means of a simulation study and the application to different datasets. Finally, the iterative use of …
Continuity correction of pearson’s chi-square test in 2x2 contingency tables: A mini-review on recent development
2022
The Pearson’s chi-square test represents a nonparametric test more used in Biomedicine and Social Sciences, but it introduces an error for 2 x 2 contingency tables, when a discrete probability distribution is approximated with a continuous distribution. The first author to introduce the continuity correction of Pearson’s chi-square test has been Yates F. (1934). Unfortunately, Yates’s correction may tend to overcorrect of p-value, this can implicate an overly conservative result. Therefore many authors have introduced variants Pearson’s chi-square statistic, as alternative continuity correction to Yates’s correction. The goal of this paper is to describe the most recent continuity correctio…
Labor Productivity Growth: Disentangling Technology and Capital Accumulation
2014
We adopt a counterfactual approach to decompose labor productivity growth into growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total Factor Productivity (TFP). We bring the decomposition to the data using international countrysectoral information spanning from the 1960s to the 2000s and a nonparametric generalized kernel method, which enables us to estimate the production function allowing for heterogeneity across all relevant dimensions: countries, sectors and time. As well as documenting substantial heterogeneity across countries and sectors, we nd average TEP to account for about 44% of labor productivity growth and TEP gaps with respect to the…
Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology
2008
Analyzing spatiotemporal dependencies between different types of events is highly relevant to many biological phenomena (e.g., signaling and trafficking), especially as advances in probes and microscopy have facilitated the imaging of dynamic processes in living cells. For many types of events, the segmented areas can overlap spatially and temporally, forming random clumps. In this paper, we model the binary image sequences of two different event types as a realization of a bivariate temporal random set and propose a nonparametric approach to quantify spatial and spatiotemporal interrelations using the pair correlation, cross-covariance, and the Ripley K functions. Based on these summary st…
Parameter Rating by Diffusion Gradient
2014
Anomaly detection is a central task in high-dimensional data analysis. It can be performed by using dimensionality reduction methods to obtain a low-dimensional representation of the data, which reveals the geometry and the patterns that exist and govern it. Usually, anomaly detection methods classify high-dimensional vectors that represent data points as either normal or abnormal. Revealing the parameters (i.e., features) that cause detected abnormal behaviors is critical in many applications. However, this problem is not addressed by recent anomaly-detection methods and, specifically, by nonparametric methods, which are based on feature-free analysis of the data. In this chapter, we provi…
Osseointegration of miniscrews: a histomorphometric evaluation.
2007
SUMMARY Mini-implants and miniscrews are commonly used in orthodontics to provide additional temporary intraoral anchorage. Partial osseointegration represents a distinct advantage in orthodontic applications, allowing effective anchorage to be combined with easy insertion and removal. This article reports the histomorphometric fi ndings of the osseointegration of bracket screw bone anchors (BSBAs). In an experimental animal study, four BSBAs were inserted in the alveolar process of the lower jaw in each of fi ve male beagle dogs, aged 6.5 months from the same mother. Eleven screws were lost during the study, nine of them due to lack of primary stability. One screw was removed at the end of…
An Analysis of Earthquakes Clustering Based on a Second-Order Diagnostic Approach
2009
A diagnostic method for space–time point process is here introduced and applied to seismic data of a fixed area of Japan. Nonparametric methods are used to estimate the intensity function of a particular space–time point process and on the basis of the proposed diagnostic method, second-order features of data are analyzed: this approach seems to be useful to interpret space–time variations of the observed seismic activity and to focus on its clustering features.