Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models

We performed benchmark exposure (BME) calculations for particulate matter when multiple dichotomous outcome variables are involved using latent class modeling techniques and generated separate results for both the extra risk and additional risk. The use of latent class models in this study is advantageous because it combined several outcomes into just two classes (namely, a high‐risk class and a low‐risk class) and compared these two classes to obtain the BME levels. This novel approach addresses a key problem in risk estimation—namely, the multiple comparisons problem, where separate regression models are fitted for each outcome variable and the reference exposure will rely on the results of the best‐fitting model. Because of the complex nature of the estimation process, the bootstrap approach was used to estimate the reference exposure level, thereby reducing uncertainty in the obtained values. The methodology developed in this article was applied to environmental data by identifying unmeasured class membership (e.g., morbidity vs. no morbidity class) among infants in utero using observed characteristics that included low birth weight, preterm birth, and small for gestational age.     

Governance structures of socially complex knowledge flows: Exchange, entitlement and gifts

The intangible aspects of knowledge transfer in multinational corporations (MNCs) continue to be a crucial area of research in social science research and raise the issue of knowledge governance structures. The purpose of this conceptual note is twofold. Firstly, we believe that there is more than one governance structure for successful knowledge transfers and flows. We provide a typology of three governance structures: “exchange,” “entitlement,” “gifts.” Secondly, most MNCs will need to take advantage of all three or a combination of these three socially complex governance structures.     

Robust decisions in power system investments

The Turkish electrical system has been studied, particularly for the near term investment programme. The objective of the study was to determine the most likely investment alternatives for the next ten-year period, from a cost effectiveness point of view. A dynamic linear programming model was used in representing the national energy system, with special emphasis on the electricity sector. Hydro, coal, nuclear and oil-fired power plants as well as their interconnection investments were modelled. A 27-year planning horizon was defined. The scenario approach was utilized in establishing the effects of different factors, such as foreign currency requirements of projects, nuclear plant costs and nuclear fuel costs, demand growth rates for electricity as well as other fuels, availability of foreign currency and skilled manpower, and the development rates of coal mines, coal power plants and nuclear power plants. After analyzing the results of 24 different scenarios, robust plans for developing the power system are suggested. Other investments that are subject to the realization of certain conditions are also indicated.     

On expected sample range from heterogeneous symmetric distributions

It is shown that for independent (but not necessarily identically distributed) random variables with distributions symmetric about the respective medians (means), the expected value of the sample range is a minimum when these means are all equal.     

Mixed probability models for dry and wet spells

When identifying the best model for representing the behavior of rainfall distribution based on a sequence of dry (wet) days, focus is usually given on the fitted model with the least number of estimated parameters. If the model with lesser number of parameters is found not adequate for describing a particular data distribution, the model with a higher number of parameters is recommended. Based on several probability models developed by previous researchers in this field, we propose five types of mixed probability models as the alternative to describe the distribution of dry (wet) spells for daily rainfall events. The mixed probability models comprise of the combination of log series distribution with three other types of models, which are Poisson distribution (MLPD), truncated Poisson distribution (MLTPD), and geometric distribution (MLGD). In addition, the combination of the two log series distributions (MLSD) and the mixed geometric with the truncated Poisson distribution (MGTPD) are also introduced as the alternative models. Daily rainfall data from 14 selected rainfall stations in Peninsular Malaysia for the periods of 1975 to 2004 were used in this present study. When selecting the best probability model to describe the observed distribution of dry (wet) spells, the Akaike’s Information Criterion (AIC) was considered. The results revealed that MLGD was the best probability model to represent the distribution of dry spells over the Peninsular.     

A note on spearman's footrule

We obtain exact tables of the null distribution of Spearman's footrule for sample sizes n = 4(1)40 by using a certain Markov chain property, and we investigate the adequacy of approximations to the distribution.     

Claim Dependence Induced by Common Effects in Hierarchical Credibility Models

In the usual credibility model, observations are made of a risk or group of risks selected from a population, and claims are assumed to be independent among different risks. However, there are some problems in practical applications and this assumption may be violated in some situations. Some credibility models allow for one source of claim dependence only, that is, across time for an individual insured risk or a group of homogeneous insured risks. Some other credibility models have been developed on a two-level common effects model that allows for two possible sources of dependence, namely, across time for the same individual risk and between risks. In this paper, we argue for the notion of modeling claim dependence on a three-level common effects model that allows for three possible sources of dependence, namely, across portfolios, across individuals and simultaneously across time within individuals. We also obtain the corresponding credibility premiums hierarchically using the projection method. Then we derive the general hierarchical structure or multi-level credibility premiums for the models with h-level of common effects.     

Bounds of moment generating functions of some life distributions

In this article we show that if a life has new better than used in expectation (NBUE) ageing property and if the mean life is finite then the moment generating function exists and is finite. In fact, the moment generating function is shown to be bounded above by that of the exponential distribution with the same mean. Analogous results are also proven for two much bigger families of life distribution, namely, the new better than renewal used in expectation (NBRUE) and the renewal new is better than used in expectation (RNBUE) and the renewal new better than renewal used in expectation (RNBRUE), provided that the life has finite two moments. Further, stronger results are also obtained for the smaller new better than used version of the above classes.     

Bayesian inference for random coefficient dynamic panel data models

We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze energy and protein intakes among individuals from the Philippines.