A Parametric Test of Perfect Ranking in Balanced Ranked Set Sampling

Many techniques based on data which are drawn by Ranked Set Sampling (RSS) scheme assume that the ranking of observations is perfect. Therefore it is essential to develop some methods for testing this assumption. In this article, we propose a parametric location-scale free test for assessing the assumption of perfect ranking. The results of a simulation study in two special cases of normal and exponential distributions indicate that the proposed test performs well in comparison with its leading competitors.

Distortion-free enhancement of terahertz signals measured by electro-optic sampling. II. Experiment

Three methods for distortion-free enhancement of electro-optic sampling measurements of terahertz signals are tested. In the first part of this two-paper series [J. Opt. Soc. Am B 31, 904–910 (2014)], the theoretical framework for describing the signal enhancement was presented and discussed. As the applied optical bias is decreased, individual signal traces become enhanced but distorted. Here we experimentally show that nonlinear signal components that distort the terahertz electric field measurement can be removed by subtracting traces recorded with opposite optical bias values. In all three methods tested, we observe up to an order of magnitude increase in distortion-free signal enhancement, in agreement with the theory, making possible measurements of small terahertz-induced transient birefringence signals with increased signal-to-noise ratio.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Sampling associated with resolvent-type kernels and lagrange-type interpolation series

In this paper a new class of Kramer kernels is introduced, motivated by the resolvent of a symmetric operator with compact resolvent. The article gives a necessary and sufficient condition to ensure that the associ- ated sampling formula can be expressed as a Lagrange-type interpolation series. Finally, an illustrative example, taken from the Hamburger moment problem theory, is included.

Theory of probability with applications /

At the head of title page: Princeton University Station, Division 2, National Defense Research Committee.

A Bayesian approach to two-stage sampling : final report for period 1 May, 1972-1 January 1976 / [prepared by W. A. Ericson].

Performing organization: Dept. of Statistics, University of Michigan.

Heavy tails, importance sampling and cross-entropy

We consider the problem of estimating P(Yi + (...) + Y-n > x) by importance sampling when the Yi are i.i.d. and heavy-tailed. The idea is to exploit the cross-entropy method as a toot for choosing good parameters in the importance sampling distribution; in doing so, we use the asymptotic description that given P(Y-1 + (...) + Y-n > x), n - 1 of the Yi have distribution F and one the conditional distribution of Y given Y > x. We show in some specific parametric examples (Pareto and Weibull) how this leads to precise answers which, as demonstrated numerically, are close to being variance minimal within the parametric class under consideration. Related problems for M/G/l and GI/G/l queues are also discussed.

Advanced mean field methods:theory and practice

A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

The process of implementing a new baby-friendly hospital initiative for nurses: a grounded theory study

Maternity nursing practice is changing across Canada with the movement toward becoming “baby friendly.” The World Health Organization (WHO) recommends the Baby-Friendly Hospital Initiative (BFHI) as a standard of care in hospitals worldwide. Very little research has been conducted with nurses to explore the impact of the initiative on nursing practice. The purpose of this study, therefore, was to examine the process of implementing the BFHI for nurses. The study was carried out using Corbin and Strauss’s method of grounded theory. Theoretical sampling was employed, which resulted in recruiting and interviewing 13 registered nurses whose area of employment included neonatal intensive care, postpartum, and labour and delivery. The data analysis revealed a central category of resisting the BFHI. All of the nurses disagreed with some of the 10 steps to becoming a baby-friendly hospital as outlined by the WHO. Participants questioned the science and safety of aspects of the BFHI. Also, participants indicated that the implementation of this program did not substantially change their nursing practice. They empathized with new mothers and anticipated being collectively reprimanded by management should they not follow the initiative. Five conditions influenced their responses to the initiative, which were (a) an awareness of a pro-breastfeeding culture, (b) imposition of the BFHI, (c) knowledge of the health benefits of breastfeeding, (d) experiential knowledge of infant feeding, and (e) the belief in the autonomy of mothers to decide about infant feeding. The identified outcomes were moral distress and division between nurses. The study findings could guide decision making concerning the implementation of the BFHI.

Resigning: a classic grounded theory of working within constraints when caring for stroke patients in the acute general care setting

This research aims to explore the challenges nurses face, when caring for stroke patients on a general medical/surgical ward, in the acute care setting and identify how nurses resolve or process this challenge. Healthcare environments continue to face the pressures of constraints such as reduced staffing levels, budgets, resources and less time, which influence care provision. Patient safety is central in care provision where nurses face the challenge of delivering best quality care when working within constraints. The incidence of stroke is increasing worldwide and internationally stroke units are the recognised minimum standard of care. In Ireland with few designated stroke units in operation many stroke patients are cared for in the acute general care setting. A classic grounded theory methodology was utilised for this study. Data was collected and analysed simultaneously through coding, constant comparison, theoretical sampling and memoing. Individual unstructured interviews with thirty two nurses were carried out. Twenty hours of non-participant observations in the acute general care setting were undertaken. The main concern that emerged was working within constraints. This concern is processed by nurses through resigning which consists of three phases; idealistic striving, resourcing and care accommodation. Through the process of resigning nurses engage in an energy maintenance process enabling them to continue working within constraints. The generation of the theory of resigning explains how nurses’ resolve or process working within constraints. This theory adds to the body of knowledge on stroke care provision. This theory has the potential to enhance nursing care, minimise burnout and make better use of resources while advocating for best care of stroke patients.

On the multi-dimensionality and sampling of air transport networks

Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled.

Flexibele Leerroutes voor Propedeusestudenten: Grounded Theory Onderzoek naar het Identificeren van Studentkenmerken in de Matching, ten behoeve van een Vraaggerichte, Gepersonaliseerde Leerroute in de Propedeuse Social Work

De Groot, D. (2016). Flexibele Leerroutes voor Propedeusestudenten: Grounded Theory Onderzoek naar het Identificeren van Studentkenmerken in de Matching, ten behoeve van een Vraaggerichte, Gepersonaliseerde Leerroute in de Propedeuse Social Work. Juli, 26, 2016, Heerlen, Nederland: Open Universiteit.

What is the Role of Attachment in Contemporary Psychotherapy? A Grounded Theory Exploration of the Perspective of Experienced Psychologists

Since Bowlby devised his theory of attachment, originally for clinical purposes, refinements and extensions have developed its clinical utility. The research question asked how experienced contemporary clinicians now perceive the role of attachment in the formulation and treatment of distress by reference to their clinical work. Using grounded theory methodology, underpinned by a relativist, moderate social constructionist epistemology, initial sampling consisted of 16 in-depth interviews with experienced clinicians. The tentative theoretical categories that emerged were then developed in theoretical sampling in further interviews with 5 of the initial interviewees. The final theoretical categories to emerge concerned the prevalence of caregiver-related problems, the provision of safety together with the prioritisation of the relationship with self as attachment-related treatment strategies, and attachment theory’s provision of understanding in problem formulation. Whilst this suggests that attachment-related ideas are integrated in contemporary practice, it also suggests that the clinical utility now offered by attachment theory, as established in the literature, has not found broad appeal amongst clinicians despite the commonness of attachment-related presenting problems. The implications of this are manifold. To begin with, attachment theorists have largely failed to bring the potential now offered by attachment-related therapeutic interventions to the market. This situation makes it incumbent on the next generation of attachment researchers to more clearly articulate techniques with which clinicians, of whatever theoretical orientation, can better leverage attachment-related knowledge in their clinical work. In this enterprise, perhaps the knowledge and experience of expert clinicians could be harvested, as this research has done. Moreover, researchers must expand the evidence base that such interventions actually work. Beyond the implications for clinical utility and efficacy, the findings strengthen counselling psychology’s influence on society’s perception and treatment of attachment-related problems.