Collective Leadership and Circles: Not Invented Here

Issues like police-community relations, violence—from neighborhood to global levels, economic inequality, and climate change have been only minimally addressed within behavior analysis, despite the oft repeated mantra that they are all at root behavioral. Disciplines determine the scope of their interests; behavior analysis and behavioral systems analysis have long claimed at least potential expertise in changing not only individual behavior, but also the collective and interlocking functioning of larger institutions and systems. In this paper we note that standard organizational behavior management (OBM) practices primarily emphasizing centralized leadership are unlikely to be adequate for such work. We therefore argue that collective leadership, a strategy that has not been emphasized in OBM, will be required to operationalize behavioral systems interventions in situations where centralized leadership is impossible or dangerous, and suggest circle processes as one behaviorally specifiable approach to constructing collective leadership, an approach that behavioral systems analysts are well-positioned to test and refine.     

Estimation in long memory time series models

A regression type estimator of the parameter d in fractionally differenced ARMA (p,q) processes is presented. The proposed estimator is shown to be mean square consistent. Its performance is compared with some of the existing estimators via a simulation study.     

Modelling Poisson marked point processes using bivariate mixture transition distributions

A class of bivariate continuous-discrete distributions is proposed to fit Poisson dynamic models in a single unified framework via bivariate mixture transition distributions (BMTDs). Potential advantages of this class over the current models include its ability to capture stretches, bursts and nonlinear patterns characterized by Internet network traffic, high-frequency financial data and many others. It models the inter-arrival times and the number of arrivals (marks) in a single unified model which benefits from the dependence structure of the data. The continuous marginal distributions of this class include as special cases the exponential, gamma, Weibull and Rayleigh distributions (for the inter-arrival times), whereas the discrete marginal distributions are geometric and negative binomial. The conditional distributions are Poisson and Erlang. Maximum-likelihood estimation is discussed and parameter estimates are obtained using an expectation–maximization algorithm, while the standard errors are estimated using the missing information principle. It is shown via real data examples that the proposed BMTD models appear to capture data features better than other competing models.     

余姚方言的语音特点

余姚方言的语音特点表现在声韵调及其拼合特点上。声母特点：保留＂帮滂並＂＂端透定＂＂见溪群＂三分格局,＂从澄崇船禅日文读＂等母今读[dz]、[z]相混,匣母逢遇摄模韵今读[v]。韵母特点：遇合三鱼虞韵知庄章日组字今读舌尖韵母,臻曾两摄舒声一等字（合口见系除外）与深臻曾梗三四等字合流,通合三与臻合三见系入声字今逢细音时合流。声调特点：古平上去浊音声母字今余姚方言合流。拼合关系也具有一定的规律性。     

Tables of the cumulative non-central cm-square distribution-part 4

The cumulative non-central chi-square .distribution is tabulated for all combinations of values of λ = 0 (0.1) 1.0 (0.2) 3.0 (0.5) 5.0 (1.0) 34.0, y=l (I) 30 (2) 50 (5) 100 and y = 0.01 (0.01) 0.1 (0.1) 1.0 (0.2) 3.0 (0.5) 10.0 (1.0 30,0 (2.0) 50,0 (5.0) 165.0. The computations have been correctly rounded to five decimal places. Also, there is a discussion about the error involved in the computations. Furthermore, there is a discussion about possible interpolation in the table using the Lagrange's method     

Computer-Aided Teaching of Probabilistic Modeling for Biological Phenomena

College students majoring in science and engineering need to learn how to model key features of the driving mechanisms of natural, scientific, and engineering phenomena. A rigorous treatment of these topics requires a thorough understanding of advanced mathematical concepts and probability theory. However, we believe that carefully designed computer simulation software offers a means of conveying fundamental ideas of probabilistic modeling, while minimizing the need for underlying mathematical analyses. Based on this premise we have initiated the development of a software system that will be incorporated into a novel, introductory course in probabilistic modeling for undergraduate students in the biological and environmental sciences. In this paper we describe the preliminary version of our system that implements simulation, real-time animation, and calculations of dynamic statistical summaries for several prototypical stochastic models for a variety of biological systems.     

Estimation and Asymptotic Properties in Periodic GARCH(1, 1) Models

This article studies the probabilistic structure and asymptotic inference of the first-order periodic generalized autoregressive conditional heteroscedasticity (PGARCH(1, 1)) models in which the parameters in volatility process are allowed to switch between different regimes. First, we establish necessary and sufficient conditions for a PGARCH(1, 1) process to have a unique stationary solution (in periodic sense) and for the existence of moments of any order. Second, using the representation of squared PGARCH(1, 1) model as a PARMA(1, 1) model, we then consider Yule-Walker type estimators for the parameters in PGARCH(1, 1) model and derives their consistency and asymptotic normality. The estimator can be surprisingly efficient for quite small numbers of autocorrelations and, in some cases can be more efficient than the least squares estimate (LSE). We use a residual bootstrap to define bootstrap estimators for the Yule-Walker estimates and prove the consistency of this bootstrap method. A set of numerical experiments illustrates the practical relevance of our theoretical results.     

Euler(p,q) Processes and Their Application to Non Stationary Time Series with Time Varying Frequencies

We introduce Euler(p, q) processes as an extension of the Euler(p) processes for purposes of obtaining more parsimonious models for non stationary processes whose periodic behavior changes approximately linearly in time. The discrete Euler(p, q) models are a class of multiplicative stationary (M-stationary) processes and basic properties are derived. The relationship between continuous and discrete mixed Euler processes is shown. Fundamental to the theory and application of Euler(p, q) processes is a dual relationship between discrete Euler(p, q) processes and ARMA processes, which is established. The usefulness of Euler(p, q) processes is examined by comparing spectral estimation with that obtained by existing methods using both simulated and real data.     

Choice between Semi-parametric Estimators of Markov and Non-Markov Multi-state Models from Coarsened Observations

Abstract.  We consider models based on multivariate counting processes, including multi-state models. These models are specified semi-parametrically by a set of functions and real parameters. We consider inference for these models based on coarsened observations, focusing on families of smooth estimators such as produced by penalized likelihood. An important issue is the choice of model structure, for instance, the choice between a Markov and some non-Markov models. We define in a general context the expected Kullback–Leibler criterion and we show that the likelihood-based cross-validation (LCV) is a nearly unbiased estimator of it. We give a general form of an approximate of the leave-one-out LCV. The approach is studied by simulations, and it is illustrated by estimating a Markov and two semi-Markov illness–death models with application on dementia using data of a large cohort study.