Results 1  10
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30
Identifying periodically expressed transcripts in microarray time series data
 Bioinformatics
, 2004
"... Motivation: Microarray experiments are now routinely used to collect largescale time series data, for example to monitor gene expression during the cell cycle. Statistical analysis of this data poses many challenges, one being that it is hard to identify correctly the subset of genes with a clear p ..."
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Cited by 44 (1 self)
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Motivation: Microarray experiments are now routinely used to collect largescale time series data, for example to monitor gene expression during the cell cycle. Statistical analysis of this data poses many challenges, one being that it is hard to identify correctly the subset of genes with a clear periodic signature. This has lead to a controversial argument with regard to the suitability of both available methods and current microarray data. Methods: We introduce two simple but efficient statistical methods for signal detection and gene selection in gene expression time series data. First, we suggest the average periodogram as an exploratory device for graphical assessment of the presence of periodic transcripts in the data. Second, we describe an exact statistical test to identify periodically
Test of significance when data are curves
 Journal of the American Statistical Association
, 1998
"... With modern technology, massive data can easily be collected in a form of multiple sets of curves. New statistical challenge includes testing whether there is any statistically significant difference among these sets of curves. In this paper, we propose some new tests for comparing two groups of cur ..."
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Cited by 32 (1 self)
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With modern technology, massive data can easily be collected in a form of multiple sets of curves. New statistical challenge includes testing whether there is any statistically significant difference among these sets of curves. In this paper, we propose some new tests for comparing two groups of curves based on the adaptive Neyman test and the wavelet thresholding techniques introduced in Fan (1996). We demonstrate that these tests inherit the properties outlined in Fan (1996) and they are simple and powerful for detecting di erences between two sets of curves. We then further generalize the idea to compare multiple sets of curves, resulting in an adaptive highdimensional analysis of variance, called HANOVA. These newly developed techniques are illustrated by using a dataset on pizza commercial where observations are curves and an analysis of cornea topography in ophthalmology where images of individuals are observed. A simulation example is also presented to illustrate the power of the adaptive Neyman test.
Identifying MMORPG bots: A traffic analysis approach
 ACE2006 (Los Angeles 14 th  16 th
, 2006
"... MMORPGs have become extremely popular among network gamers. Despite their success, one of MMORPG’s greatest challenges is the increasing use of game bots, i.e., autoplaying game clients. The use of game bots is considered unsportsmanlike and is therefore forbidden. To keep games in order, game polic ..."
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Cited by 15 (2 self)
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MMORPGs have become extremely popular among network gamers. Despite their success, one of MMORPG’s greatest challenges is the increasing use of game bots, i.e., autoplaying game clients. The use of game bots is considered unsportsmanlike and is therefore forbidden. To keep games in order, game police, played by actual human players, often patrol game zones and question suspicious players. This practice, however, is laborintensive and ineffective. To address this problem, we analyze the traffic generated by human players vs. game bots and propose solutions to automatically identify game bots. Taking Ragnarok Online, one of the most popular MMOGs, as our subject, we study the traffic generated by mainstream game bots and human players. We find that their traffic is distinguishable by: 1) the regularity in the release time
Nonparametric estimation of a periodic function
 Biometrika
, 2000
"... ABSTRACT. Motivated by applications to brightness data on periodic variable stars, we study nonparametric methods for estimating both the period and the amplitude function from noisy observations of a periodic function made at irregularly spaced times. It is shown that nonparametric estimators of pe ..."
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Cited by 14 (1 self)
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ABSTRACT. Motivated by applications to brightness data on periodic variable stars, we study nonparametric methods for estimating both the period and the amplitude function from noisy observations of a periodic function made at irregularly spaced times. It is shown that nonparametric estimators of period converge at parametric rates and attain a semiparametric lower bound which is the same if the shape of the periodic function is unknown as if it were known. Also, firstorder properties of nonparametric estimators of the amplitude function are identical to those that would obtain if the period were known. Numerical simulations and applications to real data show the method to work well in practice. KEY WORDS AND PHRASES. frequency estimation, nonparametric regression, semiparametric estimation, NadarayaWatson estimator, MACHO project, variable star data. SHORT TITLE. Estimation of a periodic function
Detecting novel associations in large data sets
 Science
, 2011
"... This copy is for your personal, noncommercial use only. If you wish to distribute this article to others, you can order highquality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following ..."
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Cited by 13 (0 self)
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This copy is for your personal, noncommercial use only. If you wish to distribute this article to others, you can order highquality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following the guidelines here. The following resources related to this article are available online at www.sciencemag.org (this infomation is current as of January 17, 2012): Updated information and services, including highresolution figures, can be found in the online version of this article at:
Robust Full Bayesian Learning for Neural Networks
, 1999
"... In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte ..."
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Cited by 12 (9 self)
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In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible jump MCMC simulated annealing algorithm to optimise neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We show that by calibrating the full hierarchical ...
Bayesian Methods for Neural Networks
, 1999
"... Summary The application of the Bayesian learning paradigm to neural networks results in a flexible and powerful nonlinear modelling framework that can be used for regression, density estimation, prediction and classification. Within this framework, all sources of uncertainty are expressed and meas ..."
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Cited by 9 (0 self)
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Summary The application of the Bayesian learning paradigm to neural networks results in a flexible and powerful nonlinear modelling framework that can be used for regression, density estimation, prediction and classification. Within this framework, all sources of uncertainty are expressed and measured by probabilities. This formulation allows for a probabilistic treatment of our a priori knowledge, domain specific knowledge, model selection schemes, parameter estimation methods and noise estimation techniques. Many researchers have contributed towards the development of the Bayesian learning approach for neural networks. This thesis advances this research by proposing several novel extensions in the areas of sequential learning, model selection, optimisation and convergence assessment. The first contribution is a regularisation strategy for sequential learning based on extended Kalman filtering and noise estimation via evidence maximisation. Using the expectation maximisation (EM) algorithm, a similar algorithm is derived for batch learning. Much of the thesis is, however, devoted to Monte Carlo simulation methods. A robust Bayesian method is proposed to estimate,
Assessing Nonstationary Time Series Using Wavelets
, 1998
"... The discrete wavelet transform has be used extensively in the field of Statistics, mostly in the area of "denoising signals" or nonparametric regression. This thesis provides a new application for the discrete wavelet transform, assessing nonstationary events in time series  especially long memory ..."
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Cited by 9 (4 self)
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The discrete wavelet transform has be used extensively in the field of Statistics, mostly in the area of "denoising signals" or nonparametric regression. This thesis provides a new application for the discrete wavelet transform, assessing nonstationary events in time series  especially long memory processes. Long memory processes are those which exhibit substantial correlations between events separated by a long period of time. Departures from stationarity in these heavily autocorrelated time series, such as an abrupt change in the variance at an unknown location or "bursts" of increased variability, can be detected and accurately located using discrete wavelet transforms  both orthogonal and overcomplete. A cumulative sum of squares method, utilizing a KolomogorovSmirnovtype
MEG adaptive noise suppression using fast LMS
 IEEE EMBS Conf Neural Eng 2005:29–32
"... AbstractMagnetoencephalography (MEG) measures magnetic fields generated by electric currents in the brain, noninvasively and with millisecond temporal resolution. Typical signals are 10 –13 T, so noise contamination due to external magnetic fields is a serious concern. Digital signal processing is ..."
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Cited by 5 (2 self)
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AbstractMagnetoencephalography (MEG) measures magnetic fields generated by electric currents in the brain, noninvasively and with millisecond temporal resolution. Typical signals are 10 –13 T, so noise contamination due to external magnetic fields is a serious concern. Digital signal processing is typically required in addition to magnetic shielding. Using three reference channels, displaced from the head, to measure the noise, we apply adaptive filtering to subtract out estimates of the noise, via the Block LeastMeanSquare (“Fast LMS”) method. The algorithm is tested by its effects on the number and distribution of channels which have statistically significant signals (distinguishable from background noise at a specified falsepositive rate). We show that Fast LMS both increases the number significant channels and reduces the variance of false positives. I.
Frequency Estimation Using UnequallySpaced Astronomical Data
, 1994
"... This thesis studies estimation of the frequency of a periodic function of time, when the function is observed with noise at a collection of unequallyspaced times. This research was motivated by the detection and classification of variable stars in astronomy. Most of the statistical literature on f ..."
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Cited by 3 (1 self)
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This thesis studies estimation of the frequency of a periodic function of time, when the function is observed with noise at a collection of unequallyspaced times. This research was motivated by the detection and classification of variable stars in astronomy. Most of the statistical literature on frequency estimation assumes equallyspaced times, but observation times in astronomy are often unequallyspaced with a sampling distribution that contains periodic effects due to being able to collect data only at certain times of day. In Chapter 1 we describe the database of variable stars collected by the MACHO collaboration and present examples which illustrate the common types of variable stars and the nature of the estimation problem. In Chapter 2 we provide background material and give models for the periodic function and sampling times. We derive the ...