A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models

We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists in coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any sample size, and does not rely on any structural assumption on the PPZ. It can be easily adapted to many versions of EM.Comment: Accepted for publication in Statistics and Computin

Reliability and validity of an iPhone® application for the measurement of lumbar spine flexion and extension range of motion

Background: Measurement of lumbar spine range of motion (ROM) is often considered to be an essential component of lumbar spine physiotherapy and orthopedic assessment. The measurement can be carried out through various instruments such as inclinometers, goniometers, and etc. Recent smartphones have been equipped with accelerometers and magnetometers, which, through specific software applications (apps) can be used for inclinometric functions. Purpose: The main purpose was to investigate the reliability and validity of an iPhone® app (TiltMeter© -advanced level and inclinometer) for measuring standing lumbar spine flexion-extension ROM in asymptomatic subjects. Design: A cross-sectional study was carried out. Setting: This study was conducted in a physiotherapy clinic located at School of Rehabilitation Sciences, Iran University of Medical Science and Health Services, Tehran, Iran. Subjects: A convenience sample of 30 asymptomatic adults (15 males; 15 females; age range = 18-55 years) was recruited between August 2015 and December 2015. Methods: Following a 2-minute warm-up, the subjects were asked to stand in a relaxed position and their skin was marked at the T12-L1 and S1-S2 spinal levels. From this position, they were asked to perform maximum lumbar flexion followed by maximum lumbar extension with their knees straight. Two blinded raters each used an inclinometer and the iPhone® app to measure lumbar spine flexion-extension ROM. A third rater read the measured angles. To calculate total lumbar spine flexion-extension ROM, the measurement from S1-S2 was subtracted from T12-L1. The second (2 hours later) and third (48 hours later) sessions were carried out in the same manner as the first session. All of the measurements were conducted 3 times and the mean value of 3 repetitions for each measurement was used for analysis. Intraclass correlation coefficient (ICC) models (3, k) and (2, k) were used to determine the intra-rater and inter-rater reliability, respectively. The Pearson correlation coefficients were used to establish concurrent validity of the iPhone® app. Furthermore, minimum detectable change at the 95 confidence level (MDC95) was computed as 1.96 � standard error of measurement � �2. Results: Good to excellent intra-rater and inter-rater reliability were demonstrated for both the gravity-based inclinometer with ICC values of �0.84 and �0.77 and the iPhone® app with ICC values of �0.85 and �0.85, respectively. The MDC95 ranged from 5.82°to 8.18°for the intra-rater analysis and from 7.38°to 8.66° for the inter-rater analysis. The concurrent validity for flexion and extension between the 2 instruments was 0.85 and 0.91, respectively. Conclusions: The iPhone®app possesses good to excellent intra-rater and inter-rater reliability and concurrent validity. It seems that the iPhone® app can be used for the measurement of lumbar spine flexion-extension ROM. © 2016 Pourahmadi et al

Estimating willingness to pay for an improved service delivery to patients referring namazi hospital chemical therapy ward in Iran using contingent valuation

Objective: The aim of this study was to estimate patients' willingness to pay (WTP) for improving the quality of non-medical aspect in Namazi hospital patients chemotherapy an assessment using the contingent valuation method (CVM). Patients and Methods: This was an applied, cross-sectional and analytical-descriptive study carried out in Iran, Shiraz in 2013. A sample of 185 patients was determined using random sampling. Multiple choice questions and follow-up open-ended questions were employed to elicit patients' WTP. The question asked patients would have to pay for this improving their own pocket. linear regression were used to Econometrically estimate the maximum WTP using STATA 11 software. Results: The results of this study indicated that 31 were male and 69 were female and the adjusted mean WTP was PPPUS15 for pat maximum amount of willing to pay was for to get the same quality service in own city respondents (PPPUS16) and minimum amount of willing to pay was to get advice of experienced nurse(PPPUS10) Patients were willing to pay more if their satisfaction with two attributes of care were increased. The cancer type and income taking care of you are significant factors influencing a patient's WTP. Conclusions: In the worst socio-economic conditions of the people were willing to pay to improve the reducing wait times in receiving a drug and get the same quality service in own city respondents. In Future efforts Health policymakers should consider the ability to pay when making their decision. © 2018 Asian Pacific Organization for Cancer Prevention

Learning a Factor Model via Regularized PCA

We consider the problem of learning a linear factor model. We propose a regularized form of principal component analysis (PCA) and demonstrate through experiments with synthetic and real data the superiority of resulting estimates to those produced by pre-existing factor analysis approaches. We also establish theoretical results that explain how our algorithm corrects the biases induced by conventional approaches. An important feature of our algorithm is that its computational requirements are similar to those of PCA, which enjoys wide use in large part due to its efficiency

Normality tests for dependent data: large-sample and bootstrap approaches

The paper considers the problem of testing for normality of the one-dimensional marginal distribution of a strictly stationary and weakly dependent stochastic process. The possibility of using an autoregressive sieve bootstrap procedure to obtain critical values and P-values for normality tests is explored. The small-sample properties of a variety of tests are investigated in an extensive set of Monte Carlo experiments. The bootstrap version of the classical skewness--kurtosis test is shown to have the best overall performance in small samples

Battle of the Attack Detection Algorithms:Disclosing cyber attacks on water distribution networks

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency increased in the past few years along with the adoption of smart water technologies. The design challenge was set for C-Town network, a real-world, medium-sized water distribution system operated through Programmable Logic Controllers and a Supervisory Control And Data Acquisition (SCADA) system. Participants were provided with datasets containing (simulated) SCADA observations, and challenged with the design of an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental & Water Resources Congress), in Sacramento, on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions

Modelling the covariance structure in marginal multivariate count models: Hunting in Bioko Island.

The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson–Tweedie distribution. Furthermore, the score information criterion is extended for selecting the components of the matrix linear predictor. We analyse a data set consisting of prey animals (the main hunted species, the blue duiker Philantomba monticola and other taxa) shot or snared for bushmeat by 52 commercial hunters over a 33-month period in Pico Basilé, Bioko Island, Equatorial Guinea. By taking into account the severely unbalanced repeated measures and longitudinal structures induced by the hunters and a set of potential covariates (which in turn affect the mean and covariance structures), our method can be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited areas anywhere in the world. We suggest that our method can be used to more accurately understand the trajectories of animals hunted for commercial or subsistence purposes and establish clear policies to ensure sustainable hunting practices