Existence of the signal in the signal plus background model

Searching for evidence of neutrino oscillations is an important problem in particle physics. Suppose that evidence for neutrino oscillations from an LSND experiment reports a significant positive oscillation probability, but that the LSND result is not confirmed by other experiments. In statistics, such a problem can be proposed as the detection of signal events in the Poisson signal plus background model. Suppose that an observed count $X$ is of the form $X=B+S$, where the background $B$ and the signal $S$ are independent Poisson random variables with parameters $b$ and $\theta$ respectively, $b$ is known but $\theta$ is not. Some recent articles have suggested conditioning on the observed bound for $B$; that is, if $X=n$ is observed, the suggestion is to base the inference on the conditional distribution of $X$ given $B\le n$. This suggestion is used here to derive an estimator of the probability of the existence of the signal event. The estimator is examined from the view of decision theory and is shown to be admissible.Comment: Published at http://dx.doi.org/10.1214/074921706000000653 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

PENGARUH MANAJEMEN LABA, KUALITAS AUDITOR, DAN DEBT DEFAULT TERHADAP PENERIMAAN OPINI GOING CONCERN

In this globalization era, adequate information is needed in taking strategic decisions. In the financial world, adequate information contained in the financial statements. Financial statements included the auditor's opinion for a company that contains information such as the existence and continuity of the entity. Auditors are expected to not only check the financial reports or detect fraud, but also be able to predict and assess the company's ability to carry out his life This study aimed to examine the effect of earnings management, quality auditor, and the acceptance of debt defaults going concern opinion. The sampling method in this research is purposive random sampling. The criteria used company is a company that has published the complete financial reports that contain the independent auditor's report and notes to the financial statements of the period 2013-2014 and received a going concern opinion. Based on the sampling criteria established during the years 2013-2014 was obtained by 50 companies. Then combined by taking a sample of companies that do not receive a going concern opinion at random sample of 50 so that there is total sample of 100 companies. The analysis tool used is SPSS 21 with logistic regression analysis (binary logistic regression). The results show that earnings management variables significantly influence the acceptance positive going concern opinion. Quality auditor significant negative effect, while debt default does not significantly influence the acceptance going concern opinion

Assessing speech, language and communication difficulties in children referred for ADHD: a qualitative evaluation of a UK child and adolescent mental health service

Background: Attention-deficit hyperactivity disorder (ADHD) is one of the most common childhood neuropsychiatric disorders and is highly comorbid with speech, language and communication difficulties (SLCDs). However, it is unclear how often SLCDs are identified in ADHD referrals in routine practice and whether there are unidentified SLCDs within this population. Method: A thematic analysis was conducted on a random sample of case notes from 18 referrals for ADHD made to a child and adolescent mental health service (CAMHS) in London, United Kingdom. Analyses aimed to identify (a) the types of SLCDs detected during assessment, (b) at which point of the episode of care these SLCDs were suspected and (c) whether a referral or consultation was made to a speech and language therapist (SLT) for further evaluation. Results: Out of 18 cases investigated, 15 were found to have possible SLCDs based on case notes and reports provided by external agencies. However, only four were referred by CAMHS for further assessment. It is unclear what, if any, steps other external agencies took. Themes describing types of SLCDs, comorbidities and the process of identification are discussed. Conclusion: The analysis of this service’s case notes revealed a range of different routes to the identification of SLCDs, and it was unclear what steps were taken as a result of assessment. A limitation is that this is just one service and the results may not generalise. However, given the similarity in practitioner training received across the country and that practitioners move from service to service, there are grounds for repeating the study in other services. We recommend a more structured approach to identifying SLCDs and recording assessment and treatment decisions made

Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft

Guide to the Dr. Jane Claire Dirks-Edmunds Papers

This collection reflects the life work of Dr. Jane Claire Dirks-Edmunds, a student and professor of Linfield College. A dedicated and scrupulous woman, the majority of the collection consists of her research, teaching materials, and correspondence. The collection also includes research and correspondence by Dr. Jane Claire Dirks-Edmunds’s mentor, Dr. James A. Macnab

On the Modeling of Musical Solos as Complex Networks

Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper including such information. arXiv admin note: text overlap with arXiv:1603.0497