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Today, crowdfunding has emerged as a popular means for fundraising. Among various crowdfunding platforms, reward-based ones are the most well received. However, to the best knowledge of the authors, little research has been performed on rewards. In this paper, we analyze a Kickstarter dataset, which consists of approximately 3K projects and 30K rewards. The analysis employs various statistical methods,...
This paper presents a framework of automatic clustering to determine correctly matched keypoints locations in aerial images for visual-based attitude estimation. In this work, correct and false matches are automatically identified using a clustering technique which utilizes the outlier information to determine the initial number of clusters and cross-correlation. The proposed framework has been tested...
We define and compare statistical characteristics of estimate of mean power of signals reflected from meteorological formations (MF). Their dependences on training sample size, power of MF, type and level of interperiod correlation of reflections from MF are analyzed. Rational ways of practical implementation of MF mean power estimate are demonstrated.
We define and compare the statistical characteristics of the estimates of Doppler velocity spectrum (DVS) width of meteorological formations (MF) based on the various modifications of “pulse pair” method. For the conditions, when the form of unimodal DVS differs from the Gaussian one, the errors of proposed modification of “pulse pair” method are evaluated, and possible ways to reduce them are proposed...
The ratio of NPLs (non-performance loan) is one of the important indexes of assessing the security of credit assets, as well as it is an essential financial indicator to overall strength in commercial banks. In this paper we establish a time series model for the ratio of NPLs of Chinese state-owned commercial banks, taking the statistical data of the NPLs from 2002 to 2010 as an example, basing on...
In order to study the spectral density properties of 900MHz radio signal, the time series theory is introduced into statistical analysis. By means of the statistical data from signal acquisition system, the spectral density analysis principle is introduced. Moreover, both the autoregressive (AR) spectral estimation and autoregressive moving average (ARMA) spectral estimation are taken into spectral...
Random process variations are often composed of location dependent part and distance dependent correlated part. While an accurate extraction of process variation is a prerequisite of both process improvement and circuit performance prediction, it is not an easy task to characterize such complicated spatial random process from a limited number of silicon data. For this purpose, kriging model was introduced...
We use a collection of simple models to examine the interaction between the variance reduction technique of common random numbers and a new simulation metamodeling technique called stochastic kriging. We consider the impact of common random numbers on prediction, parameter estimation and gradient estimation.
Maternal dyslipidemia in preeclampsia is well established. Serum lipid levels as potential predictors of preeclampsia are yet to be investigated. Discriminant analysis and k-means clustering were used to predict preeclampsia (PE) based on the contribution of lipid parameters. Serum total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C) and triglycerides (TG) were...
To find effective estimations of tail dependence, we present the estimators of upper tail dependence coefficient by using survival copula. We research to two problems by using the samples from t-copula. Firstly, do the estimators estimate effectively the upper tail dependence coefficients of copula? Which is the best among the estimators? Secondly, if sample isn't from true distribution, do the estimators...
Music mood estimation (MME) is an emerging subfield in music information retrieval research. Whereas most MME research focuses on audio analysis, exploring the significance of lyrics in predicting song emotion has been receiving more attention in recent years. One major impediment to MME research is the lack of clearly-labeled and publicly-available datasets of separately annotated lyrics and audio...
Despite their small size, analog/mixed-signal circuits start with an extensive set of parameters to test for. During production ramp up, most of these tests are dropped using statistical analysis techniques based on the dropout patterns. While effective in reducing the number of tests, this approach treats each device in an identical manner. As the statistical diversity of the devices increases due...
Over the past few years, a considerable number of studies have focused on estimation of arterial stiffness by using a digital volume pulse (DVP). This can generally be achieved by a linear regression analysis with several pulse parameters. Among various pulse parameters, since time-related pulse parameters, such as reflected wave arrival time, are varied with pulse rate, it can provide incorrectly...
This paper explores a new technique of load modeling and estimation on distribution systems. With the emerging AMI technology real-time data about customer loads will be available and hence an estimate of loads on the distribution feeder can be made for system monitoring and control. With this data, a load model predicting the real-time load variations can be made. This paper elaborates the statistical...
This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator...
The Grassberger-Procaccia (GP) algorithm is investigated in estimating ID of hyperspectral imagery. Due to the high data dimensionality and large pairwise pixel distance, data dimensionality may need to be pre-reduced such that the trade-off can be achieved between taking the scale r small enough to have an accurate estimate and taking the r sufficiently large to reduce statistical errors due to lack...
Statistical Static Timing Analysis (SSTA) is becoming necessary, but has not been widely adopted due to various weaknesses. In this paper, we address one of the challenging problems in SSTA: computation of correlations between cell delays. With the help of conditional moments, cell-to-cell and path-to-path delay correlations are computed by propagating iteratively means and variances of cell delays...
The empirical results show that the dynamic conditional correlation (DCC) and the bivariate IGARCH (1, 1) model is appropriate in evaluating the relationship of the Thailand and the Philippine's stock markets under the oil price returns of the high oil price periods. The empirical result also indicates that the Thailand and the Philippine's stock markets is a positive relation. The average estimation...
This paper presents an approach for ordering analog specification (or functional) tests that is based on a statistical estimation of parametric defect level. A statistical model of n specification tests is obtained by applying a density estimation technique to a small sample of data (obtained from the initial phase of production testing or through Monte-Carlo simulation of the design). The statistical...
We present an asymptotic statistical analysis of the Hyperanalytic Wavelet Transform (HWT) coefficients resulted after the estimation of their inter-scale and inter-band dependency. The resulting equations are useful for the design of different signal processing systems based on the wavelet theory used in communications.
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