Constrained-Realization Monte-Carlo Method for Hypothesis Testing

We compare two theoretically distinct approaches to generating artificial (or ``surrogate'') data for testing hypotheses about a given data set. The first and more straightforward approach is to fit a single ``best'' model to the original data, and then to generate surrogate data sets that are ``typical realizations'' of that model. The second approach concentrates not on the model but directly on the original data; it attempts to constrain the surrogate data sets so that they exactly agree with the original data for a specified set of sample statistics. Examples of these two approaches are provided for two simple cases: a test for deviations from a gaussian distribution, and a test for serial dependence in a time series. Additionally, we consider tests for nonlinearity in time series based on a Fourier transform (FT) method and on more conventional autoregressive moving-average (ARMA) fits to the data. The comparative performance of hypothesis testing schemes based on these two approaches is found to depend on whether or not the discriminating statistic is pivotal. A statistic is ``pivotal'' if its distribution is the same for all processes consistent with the null hypothesis. The typical-realization method requires that the discriminating statistic satisfy this property. The constrained-realization approach, on the other hand, does not share this requirement, and can provide an accurate and powerful test without having to sacrifice flexibility in the choice of discriminating statistic.Comment: 19 pages, single spaced, all in one postscript file, figs included. Uncompressed .ps file is 425kB (sorry, it's over the 300kB recommendation). Also available on the WWW at http://nis-www.lanl.gov/~jt/Papers/ To appear in Physica

Analisis Regresi Semiparametrik Pada Kasus Hilangnya Respon

In the specific cases of experiment, not all data (response) may be available, which is called missing response cases. It's appear for various reasons. For the existing problem, inference statistics cannot be applied directly. The aim of this research is to consider about certain method to impute the missing response which is related to semiparametric regression, as a goodness of fit measurement of the used method, suppose an estimator which is compared to the mean of complete response, then consider asymptotic distribution, consistency and efficiency of parametrics component estimator. By using Kernel approximation, the resulted of nonparametrics estimator and by least square method, the resulted parametric component .The application to minimum temperature's data in 56 cities at USA, estimator value of for several confidence interval tend to be similar to the mean value of complete response

Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

Active learning of statistics: A case study

Research at the Open University has investigated students’ learning of statistical concepts and how information technology can be effectively used to support this process. Previous empirical work has looked at psychology students’ misconceptions relating to correlation and how computer‐based learning environments can be used to address these. This paper reports on the findings from a qualitative study that investigated students’ learning collaboratively from a multimedia application called ActivStats