Automating the Calibration of a Neonatal Condition Monitoring System

Abstract. Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration

Missing values: sparse inverse covariance estimation and an extension to sparse regression

We propose an l1-regularized likelihood method for estimating the inverse covariance matrix in the high-dimensional multivariate normal model in presence of missing data. Our method is based on the assumption that the data are missing at random (MAR) which entails also the completely missing at random case. The implementation of the method is non-trivial as the observed negative log-likelihood generally is a complicated and non-convex function. We propose an efficient EM algorithm for optimization with provable numerical convergence properties. Furthermore, we extend the methodology to handle missing values in a sparse regression context. We demonstrate both methods on simulated and real data.Comment: The final publication is available at http://www.springerlink.co

Heritability of attention problems in children II: longitudinal results from a study of twins age 3 to 12.

this paper we present data of large samples of twin families, with an equal number of girls and boys. The well-known gender difference with boys displaying more OA and AP was observed at each age. Even at the age of 3, boys display more OA problems than girls. Clinical studies have indicated that severe problem behavior can be identified in very young children (see for review, Campbell, 1995; Keenan & Wakschlag, 2000; Shaw, Owens, Giovannelli, & Winslow, 2001) and that the onset of ADHD is during the pre-school period (Barkley, Fisher, Edelbrock, & Smallish, 1990; Table 6 Top part includes percentages of total variances (diagonal) and covariances (off-diagonal) explained by additive genetic, genetic dominance, and unique environmental components based on best fitting models. Percentages for boys and girls are reported below and above diagonal, respectively. Lower part includes correlations calculated for additive genetic, genetic dominance, and unique environmental sources of variance between different ages. Correlations for boys and girls are reported below and above diagonal, respectively Relative proportions of variance and covariance BoysnGirls A% D% E% OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 50n41 73 79 75 22n33 17 13 14 28n26 10 8 11 AP 7 59 33n57 50 53 31 39n16 31 28 10 28n27 19 19 AP 10 86 31 41n48 47 6 51 31n25 32 8 18 28n27 21 AP 12 71 24 31 40n54 16 55 45 30n18 13 21 24 30n28 Correlations between different ages BoysnGirls ADE OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 1.00 .60 .66 .57 1.00 .30 .16 .20 1.00 .15 .12 .14 AP 7 .57 1.00 .62 .57 .41 1.00 .99 1.00 .15 1.00 .46 .41 AP 10 .68 .56 1.00 .61 .08 .94 1.00 1.00 .11 .42 1.00 .50 AP 12 .49 .42 .53 1.00 .20 .98 .99 1.00 .14 .45 .58 1.00 ..

On the Construction of Imputation Classes in Surveys

This paper explores the problem of the construction of imputation classes using the score method, sometimes called predictive mean stratification or response propensity stratification, depending on the context. This method was studied in Thomsen (1973), Little (1986) and Eltinge & Yansaneh (1997). We use a different framework to evaluate the properties of the resulting imputed estimator of a population mean. In our framework, we condition on the realized sample. This enables us to considerably simplify our theoretical developments in the frequent situation where the boundaries and the number of classes are sample-dependent. We find that the key factor for reducing the non-response bias is to form classes homogeneous with respect to the response probabilities and/or the conditional expectation of the variable of interest. In the latter case, the non-response/imputation variance is also reduced. Finally, we performed a simulation study to fully evaluate various versions of the score method and to compare them with a cross-classification method, which is frequently used in practice. The results showed the superiority of the score method in general. Copyright 2007 The Authors. Journal compilation (c) 2007 International Statistical Institute.