Climate regime shift detection with a trans‐dimensional,sequential Monte Carlo,variational Bayes method

We present an application study which exemplifies a cutting edge statistical approach for detecting climate regime shifts. The algorithm uses Bayesian computational techniques that make time‐efficient analysis of large volumes of climate data possible. Output includes probabilistic estimates of the number and duration of regimes, the number and probability distribution of hidden states, and the probability of a regime shift in any year of the time series. Analysis of the Pacific Decadal Oscillation (PDO) index is provided as an example. Two states are detected: one is associated with positive values of the PDO and presents lower interannual variability, while the other corresponds to negative values of the PDO and greater variability. We compare this approach with existing alternatives from the literature and highlight the potential for ours to unlock features hidden in climate data.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.     

Smoothed empirical likelihood confidence intervals for the relative distribution with left‐truncated and right‐censored data

The study of differences among groups is an interesting statistical topic in many applied fields. It is very common in this context to have data that are subject to mechanisms of loss of information, such as censoring and truncation. In the setting of a two‐sample problem with data subject to left truncation and right censoring, we develop an empirical likelihood method to do inference for the relative distribution. We obtain a nonparametric generalization of Wilks' theorem and construct nonparametric pointwise confidence intervals for the relative distribution. Finally, we analyse the coverage probability and length of these confidence intervals through a simulation study and illustrate their use with a real data set on gastric cancer. The Canadian Journal of Statistics 38: 453–473; 2010 © 2010 Statistical Society of Canada     

Being Under Time Pressure: The Case of Workers with Disabilities

This article examines levels of time stress reported by people with and without disabilities. Using data at an individual level from the Time Use Survey for Spain in 2002–2003, we estimate an ordered probit model to investigate the determinants of time stress by disability status. We find that disabled individuals work fewer hours, have more free time and engage in more household labour as compared to the non-disabled. The estimation results show that disabled workers (especially those who are severely or moderately limited in their daily activities) suffer from more stress than their non-disabled counterparts. In addition, longer working hours increase the levels of time stress reported by all individuals, but more intensely so among disabled workers.     

Cambodia: Reconstructing the Demographic Stab of the Past and Forecasting the Demographic Scar of the Future

Cambodia went through 5 years of violent internal conflicts (1970–1975), including the spread of the American–Vietnamese war into the country, followed by four more years of brutal and chaotic Khmer Rouge government (1975–1979). The result was almost 2 million deaths. This article attempts to contribute to the reconstruction of the demographic history of Cambodia. This is done in three ways. First, by analysing scars in the pyramids corresponding to the recent past, present and future; second, by estimating major demographic events during the 1970s, especially excess deaths; and third, by comparing the size and composition of a simulated normal population with the real population.     

Integrative public leadership: Catalyzing collaboration to create public value

Integrative public leadership is a process of developing partnerships across organizational, sectoral and/or jurisdictional boundaries that create public value. This paper explores the concept in the context of the literature and illustrates some salient features of integrative public leadership through three cases involving extensive multi-sector collaboration in the western (Smoky Mountain) region of North Carolina. The cases are different in subject matter—sewer lines to a rural community, broadband infrastructure across a network of rural schools and colleges, and a major environmental preservation effort—but they all share some key elements. Leadership in each case is enacted through structure, process, and people. Boundary organizations provide a structural context for partnership development; boundary experiences and boundary objects serve to bridge differences and create a common purpose; and boundary spanners exhibit entrepreneurial qualities and leverage relationship capital in order to facilitate integration.     

Adaptive and Generative Learning: Implications from Complexity Theories

One of the most important classical typologies within the organizational learning literature is the distinction between adaptive and generative learning. However, the processes of these types of learning, particularly the latter, have not been widely analyzed and incorporated into the organizational learning process. This paper puts forward a new understanding of adaptive and generative learning within organizations, grounded in some ideas from complexity theories: mainly self‐organization and implicate order. Adaptive learning involves any improvement or development of the explicate order through a process of self‐organization. Self‐organization is a self‐referential process characterized by logical deductive reasoning, concentration, discussion and improvement. Generative learning involves any approach to the implicate order through a process of self‐transcendence. Self‐transcendence is a holo‐organizational process characterized by intuition, attention, dialogue and inquiry. The main implications of the two types of learning for organizational learning are discussed.     

The Role of Automatic Stabilizers in the U.S. Business Cycle

Most countries have automatic rules in their tax‐and‐transfer systems that are partly intended to stabilize economic fluctuations. This paper measures their effect on the dynamics of the business cycle. We put forward a model that merges the standard incomplete‐markets model of consumption and inequality with the new Keynesian model of nominal rigidities and business cycles, and that includes most of the main potential stabilizers in the U.S. data and the theoretical channels by which they may work. We find that the conventional argument that stabilizing disposable income will stabilize aggregate demand plays a negligible role in the dynamics of the business cycle, whereas tax‐and‐transfer programs that affect inequality and social insurance can have a larger effect on aggregate volatility. However, as currently designed, the set of stabilizers in place in the United States has had little effect on the volatility of aggregate output fluctuations or on their welfare costs despite stabilizing aggregate consumption. The stabilizers have a more important role when monetary policy is constrained by the zero lower bound, and they affect welfare significantly through the provision of social insurance.