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Clustered survival data

"Smooth" Inference for Clustered Survival Data

"Smooth" Inference for Clustered Survival Data

... the survival functions by Kaplan-Meier ...nonparametric survival models, and the joint density is constructed via a ...bivariate survival function is based on kernel ...

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A comparison of three prediction modelling approaches for clustered survival data with application to Lynch Syndrome Family

A comparison of three prediction modelling approaches for clustered survival data with application to Lynch Syndrome Family

... the data, so that we include analyses based on these three types in Section ...family data are summarized in Tables ...the data set had at least one adenoma detection and removal during their ...

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Deng_unc_0153D_16042.pdf

Deng_unc_0153D_16042.pdf

... Change-point effects have been observed in many epidemiology studies. The identified change points of certain biomakers are applied to predict disease risks. Such risk scores may influence the decision of early ...

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Parametric frailty models for clustered data with arbitrary censoring: application to effect of male circumcision on HPV clearance

Parametric frailty models for clustered data with arbitrary censoring: application to effect of male circumcision on HPV clearance

... censored data (including left, interval and right censoring) can be used ...censored data, what can further complicate the analysis is the presence of clustered data where the study subjects ...

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Time Window and Location Based Clustered Routing with Big and Distributed Data

Time Window and Location Based Clustered Routing with Big and Distributed Data

... Big data is the data that traditional data management tools cannot handle or have a hard time processing it ...“big data” can mean a very broad set of data structures; from CSVs to log ...

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Arpino Stata 2018

Arpino Stata 2018

... Propensity score matching with clustered data in Stata.. Bruno Arpino.[r] ...

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Ant Possibilistic Fuzzy Clustered Forecasting on High Dimensional Data

Ant Possibilistic Fuzzy Clustered Forecasting on High Dimensional Data

... Efficient prediction of stock market for financial analysis not only serves for the short-tem investors but it is also an efficient means for long-term investors too. Principal Component Analysis was applied in [11] ...

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A Survey on Clustered Feature Selection
          Algorithms for High Dimensional Data

A Survey on Clustered Feature Selection Algorithms for High Dimensional Data

... The complete graph reflects the correlations among all the target-relevant features. Unfortunately, graph has vertices and ( −1)/2 edges. For high dimensional data, it is heavily dense and the edges with different ...

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The prediction accuracy of dynamic mixed-effects models in clustered data

The prediction accuracy of dynamic mixed-effects models in clustered data

... modeling clustered data is with generalized linear mixed- effects models (GLMM), which use random effects to parameterize heterogeneity in ef- fects across clusters and induce a within-cluster correlation ...

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Clustered Data Storage for Multi-Site Fusion Experiments

Clustered Data Storage for Multi-Site Fusion Experiments

... groups has been implemented by a combination of database application accounts dedicated to each site group and access permissions to registered IP addresses. Each stored data set belongs to its own site, and also ...

5

Assessing discriminative ability of risk models in clustered data

Assessing discriminative ability of risk models in clustered data

... Methods: We used data of the CRASH trial (2,081 patients clustered in 35 centers) to develop a risk model for mortality after traumatic brain injury. To assess the discriminative ability of the risk model ...

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Some Inference Problems in Clustered (Longitudinal) Count Data with Over-dispersion

Some Inference Problems in Clustered (Longitudinal) Count Data with Over-dispersion

... In Chapter 3 we developed score tests for homogeneity between and within groups for over-dispersed count data, presented simulation results and showed some examples. In the examples, we saw that the null ...

118

DGEclust: differential expression analysis of clustered count data

DGEclust: differential expression analysis of clustered count data

... where N is the number of genes in the dataset. The sim- ilarity matrix calculated as above was used to construct the dendrograms and heat map in Figure 8, after employ- ing a Euclidean distance metric and average ...

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DESIGN OF ARTIFICIAL NEURAL NETWORK BASED DATA AGGREGATION IN CLUSTERED WSNS

DESIGN OF ARTIFICIAL NEURAL NETWORK BASED DATA AGGREGATION IN CLUSTERED WSNS

... the data aggregation. In this proposed method the efficiency of data gathering is improved and the total energy consumption is ...the data aggregation in multihop wireless mesh sensor neural ...based ...

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Using genotyping to delineate tuberculosis transmission in long term care facilities: single facility 4 year experience

Using genotyping to delineate tuberculosis transmission in long term care facilities: single facility 4 year experience

... 4 clustered TB infections, but also identified 12 subjects without epidemiological linkage in cluster ...with clustered infection from co- incidental TB subjects in population with high TB inci- dence ...

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Inference of Non-Overlapping Camera Network Topology using Statistical Approaches

Inference of Non-Overlapping Camera Network Topology using Statistical Approaches

... The object detector reads the video and analyzes the entry/exit location as well as the time of the moving objects. The output of this step is a text file with all observations. Then we cluster the entry/exit points for ...

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Optimal Weights in Nonparametric Analysis of Clustered ROC Curve Data

Optimal Weights in Nonparametric Analysis of Clustered ROC Curve Data

... In diagnostic trials, clustered data are obtained when several subunits of the same patient are ob- served. Within-cluster correlations need to be taken into account when analyzing such clustered ...

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Fixed effects inference for clustered data in Gaussian linear models

Fixed effects inference for clustered data in Gaussian linear models

... for clustered data assume balanced data; this power analysis gives a way to compute power for unbalanced ...unbalanced data, so this method gives a power analysis aligned with a defensible ...

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Meaningful regression and association models for clustered ordinal data

Meaningful regression and association models for clustered ordinal data

... Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, often the association structure is of ...

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Mining on K way Means Clustered Streaming Data

Mining on K way Means Clustered Streaming Data

... In this paper we are using k-way means clustering algorithm to cluster these kind of streaming data based on key performance indicators [4]. K-mean clustering is of vector quantization initially from signal ...

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