We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcod...
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We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse. More information is available at https://***/poolseq/
In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution which can capture both over and under dispersion that is usually encounte...
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In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right-censored data, the main contribution is in developing the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters of the flexible Conway-Maxwell Poisson cure rate model with Weibull lifetimes. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway-Maxwell Poisson distribution is addressed using the likelihood ratio test and information-based criteria to select a suitable competing cause distribution that provides the best fit to the data. A simulation study is also carried out to demonstrate the loss in efficiency when selecting an improper competing cause distribution which justifies the use of a flexible family of distributions for the number of competing causes. Finally, the proposed methodology and the flexibility of the Conway-Maxwell Poisson distribution are illustrated with two known data sets from the literature: smoking cessation data and breast cosmesis data.
作者：
Teraguchi, ShunsukeKumagai, YutaroTohoku Univ
Tohoku Med Megabank Org Aoba Ku 2-1 Seiryo Machi Sendai Miyagi 9808573 Japan Osaka Univ
Immunol Frontier Res Ctr Quantitat Immunol Res Unit 3-1 Yamada Oka Suita Osaka 5650871 Japan
Background: Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information...
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Background: Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. Methods: To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation. Results: We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements. Conclusions: The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors.
This paper considers the statistical analysis of masked data in a series system with Burr-XII distributed components. Based on progressively Type-I interval censored sample, the maximum likelihood estimators for the p...
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This paper considers the statistical analysis of masked data in a series system with Burr-XII distributed components. Based on progressively Type-I interval censored sample, the maximum likelihood estimators for the parameters are obtained by using the expectation maximization algorithm, and the associated approximate confidence intervals are also derived. In addition, Gibbs sampling procedure using important sampling is applied for obtaining the Bayesian estimates of the parameters, and Monte Carlo method is employed to construct the credible intervals. Finally, a simulation study is proposed to illustrate the efficiency of the methods under different removal schemes and masking probabilities.
The Automatic Identification System (AIS) is an automatic tracking system which has been widely applied in the fields of intelligent transportation systems, e.g., collision avoidance, navigation, maritime supervision ...
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ISBN:
(纸本)9781509059546
The Automatic Identification System (AIS) is an automatic tracking system which has been widely applied in the fields of intelligent transportation systems, e.g., collision avoidance, navigation, maritime supervision and management. Compare with other positioning systems, e.g., very high frequency (VHF) and radar, AIS can conquer the human errors and it is almost not affected by the external environment. To make better use of the AIS data, it is necessary to statistically analyze the massive AIS trajectories. The statistical results could make us better understand the potential properties of AIS trajectories. It is well known that most current practical applications are strongly dependent on the geometrical structures of AIS trajectories. In this paper, a Gaussian Mixture Model (GMM) is introduced to investigate the longitude and latitude differences of AIS trajectory data. The parameters of GMM are estimated using the expectationmaximization (EM) algorithm. The experimental results have illustrated the superior performance of our proposed method.
An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest p...
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To address multi-sensor robust track-to-track association in the presence of sensor biases and missed detections, where sensors biases is time-varying and non-uniform, the target of different sensors is non-identical,...
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ISBN:
(纸本)9780996452700
To address multi-sensor robust track-to-track association in the presence of sensor biases and missed detections, where sensors biases is time-varying and non-uniform, the target of different sensors is non-identical, the robust track-to-track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. Firstly, the orthogonal normalization reduce the general affine case of track point set;second, the heavy-tailed t-distribution mixture model is established with better robustness to tracks of non-common, solved by expectationmaximization (EM) algorithm. The conditional expectation function is added a regular item of point sets that the points have a feature of Coherent Point Drift (CPD). Adaptability experiments are established to demonstrate the effectiveness of the proposed approaches compared with competing
A novel statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical channels in the presence of air bubbles for fresh and salty waters, based on experimental data. In this mo...
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ISBN:
(纸本)9781509050192
A novel statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical channels in the presence of air bubbles for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture ExponentialGamma distribution. We use the expectationmaximization (EM) algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all the channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, the application of the Exponential-Gamma model leads to closed-form and analytically tractable expressions for key system performance metrics such as the outage probability and the average bit-error rate.
Ratings from multiple human annotators are often pooled in applications where the ground truth is hidden. Examples include annotating perceived emotions and assessing quality metrics for speech and image. These rating...
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ISBN:
(纸本)9781510833135
Ratings from multiple human annotators are often pooled in applications where the ground truth is hidden. Examples include annotating perceived emotions and assessing quality metrics for speech and image. These ratings are not restricted to a single dimension and can be multidimensional. In this paper, we propose an expectation-maximization based algorithm to model such ratings. Our model assumes that there exists a latent multidimensional ground truth that can be determined from the observation features and that the ratings provided by the annotators are noisy versions of the ground truth. We test our model on a study conducted on children with autism to predict a four dimensional rating of expressivity, naturalness, pronunciation goodness and engagement. Our goal in this application is to reliably predict the individual annotator ratings which can be used to address issues of cognitive load on the annotators as well as the rating cost. We initially train a baseline directly predicting annotator ratings from the features and compare it to our model under three different settings assuming: (i) each entry in the multidimensional rating is independent of others, (ii) a joint distribution among rating dimensions exists, (iii) a partial set of ratings to predict the remaining entries is available.
An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest p...
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An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Hayes' rule for quality estimation However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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