Predicting infectious disease outbreak risk via migratory waterfowl vectors

The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.     

The wrapped skew-normal distribution on the circle

The wrapped skew-normal distribution is proposed as a model for circular data. Basic results for the distribution are established and estimation for a circular parametrisation of it considered. Procedures based on the sample second central sine moment for testing for departures from three important limiting cases of the distribution are described. The model and some new inferential techniques are applied to directional data from a study into bird migration.     

Spatio-temporal Trend Analysis of Spring Arrival Data for Migratory Birds

There is increasing interest in spatio-temporal analysis of environmental and ecological responses to changes in the climate due to the recent concerns about climate change. In this work, we propose a spatio-temporal modeling framework for analyzing environmental and ecological data while accounting for spatial and temporal structure, as well as climate effects. As an example, we consider data on bird migration in the United States and analyze the spring arrival dates of Purple Martins between historical data (1905–1940) from the North American Bird Phenology Program and recent data (2001–2010) from the Purple Martin Conservation Association. The proposed approach allows researchers to compare mean arrival dates while accounting for spatial and temporal variability. Our results for Purple Martins showed statistically significant earlier spring arrivals in parts of United States over the recent years. The proposed approach provides a useful tool for statistical analysis of spatio-temporal data related to studies of climate change.     

Abundance estimation with a categorical covariate subject to missing in continuous-time capture-recapture studies

Abstract

In continuous-time capture-recapture experiments, individual heterogeneity has a large effect on the capture probability. To account for the heterogeneity, we consider an individual covariate, which is categorical and subject to missing. In this article, we develop a general model to summarize three kinds of missing mechanisms, and propose a maximum likelihood estimator of the abundance. A likelihood ratio confidence interval of the abundance is also proposed. We illustrate the proposed methods by simulation studies and a real data example of a bird species prinia subflava in Hong Kong.     

Extraction of sea temperature in the Barents Sea by a scale space multiresolution method – prospects for Atlantic salmon

Variation of marine temperature at different time scales is a central environmental factor in the life cycle of marine organisms, and may have particular importance for various life stages of anadromous species, for example, Atlantic salmon. To understand the salient features of temperature variation we employ scale space multiresolution analysis, that uses differences of smooths of a time series to decompose it as a sum of scale-dependent components. The number of resolved components can be determined either automatically or by exploring a map that visualizes the structure of the time series. The statistical credibility of the features of the components is established with Bayesian inference. The method was applied to analyze a marine temperature time series measured from the Barents Sea and its correlation with the abundance of Atlantic salmon in three Barents Sea rivers. Besides the annual seasonal variation and a linear trend, the method revealed mid time-scale (～10 years) and long time-scale (～30 years) variation. The 10-year quasi-cyclical component of the temperature time series appears to be connected with a similar feature in Atlantic salmon abundance. These findings can provide information about the environmental factors affecting seasonal and periodic variation in survival and migrations of Atlantic salmon and other migratory fish.     

跨方言区流动、自选择与劳动力收入

本文基于2012和2014年中国劳动力动态调查（CLDS）数据,采用内生转换回归模型考察劳动力跨方言区流动对收入的影响。实证结果表明,与在同一方言区流动的个体相比,跨方言区流动收入要高31.9%;对于跨方言区流动的劳动力而言,跨方言区流动对收入的平均处理效应为30.5%,如果在同一方言区流动的劳动力选择跨方言区流动,其收入反而会减少3.9%,说明不同个体在流入地的选择上是基于其比较优势做出的决策。进一步,本文还发现跨方言区流动的劳动力无论选择在同一方言区流动还是跨方言区流动,均能获得较高的收入,在流动决策上具有绝对优势。在考虑了测量误差和极端值以及排除其他干扰因素后,上述结论依然稳健。     

Migration pattern in Bangladesh: a covariate-dependent Markov model

Internal migration is one of the major components of rapid and unplanned growth of towns and cities especially in the developing countries. This paper describes the transition pattern of internal out migration in Bangladesh and some sociodemographic factors influencing such migration in the country using a covariate-dependent Markov model. Four types of migration behavior namely, rural to rural, rural to urban, urban to rural and urban to urban are under consideration of this paper. Defining two discrete states, urban and rural, each of such transition can be characterized by a stochastic process; hence we use a two-state Markov chain for this purpose. We find that age, sex, division and reason of migration are significantly associated with internal migration in Bangladesh. The major findings include that any type of migration, rural to rural, rural to urban, urban to rural and urban to urban, mostly take place at the ages of 15–30 as well as at the ages of 0–15; females have higher odds than males to make a migration; Dhaka, Rajshahi and Chittagong divisions have remarkably higher migration rate as compared to Barisal and Sylhet division; and the professional reason is the main reason for rural to urban migration.     

Changes in the geographical mobility of the population of Poland

Spatial mobility in Poland is analyzed for the period 1976-1989. The focus is on the decline in internal migration over time and its causes. The analysis includes migration between rural and urban areas as well as migration among voivodships. Factors affecting migration include changes in the age distribution; however, the authors conclude that changes in migration patterns are primarily due to socioeconomic factors, particularly the economic crises the country has faced in recent years.     

Lifetime Assessment by Intermittent Inspection under the Mixture Weibull Power Law Model with Application to XLPE Cables

This paper proposes a new treatment for electrical insulation degradation. Some types of insulation which have been used under various circumstances are considered to degrade at various rates in accordance with their stress circumstances. The cross-linked polyethylene (XLPE) insulated cables inspected by major Japanese electric companies clearly indicate such phenomena. By assuming that the inspected specimen is sampled from one of the clustered groups, a mixed degradation model can be constructed. Since the degradation of the insulation under common circumstances is considered to follow a Weibull distribution, a mixture model and a Weibull power law can be combined. This is called The mixture Weibull power law model. By using the maximum likelihood estimation for the newly proposed model to Japanese 22 and 33kV insulation class cables, they are clustered into a certain number of groups by using the AIC and the generalized likelihood ratio test method. The reliability of the cables at specified years are assessed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Random balanced resampling: A new method for estimating variance components in unbalanced designs

Variance components in factorial designs with balanced data are commonly estimated by equating mean squares to expected mean squares. For unbalanced data, the usual extensions of this approach are the Henderson methods, which require formulas that are rather involved. Alternatively, maximum likelihood estimation based on normality has been proposed. Although the algorithm for maximum likelihood is computationally complex, programs exist in some statistical packages. This article introduces a simpler method, that of creating a balanced data set by resampling from the original one. Revised formulas for expected mean squares are presented for the two-way case; they are easily generalized to larger factorial designs. The results of a number of simulation studies indicate that, in certain types of designs, the proposed method has performance advantages over both the Henderson Method I and maximum likelihood estimators.     

The measurement of the intensity of internal migration

The method developed by Daniel Courgeau for calculating rates of internal migration is described and applied to data for Poland. Individual probabilities of internal migration are calculated for various years, population groups, and administrative areas in order to show that these probabilities are not constant.     

A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Flood events can be caused by several different meteorological circumstances. For example, heavy rain events often lead to short flood events with high peaks, whereas snowmelt normally results in events of very long duration with a high volume. Both event types have to be considered in the design of flood protection systems. Unfortunately, all these different event types are often included in annual maximum series (AMS) leading to inhomogeneous samples. Moreover, certain event types are underrepresented in the AMS. This is especially unsatisfactory if the most extreme events result from such an event type. Therefore, monthly maximum data are used to enlarge the information spectrum on the different event types. Of course, not all events can be included in the flood statistics because not every monthly maximum can be declared as a flood. To take this into account, a mixture Peak-over-threshold model is applied, with thresholds specifying flood events of several types that occur in a season of the year. This model is then extended to cover the seasonal type of the data. The applicability is shown in a German case study, where the impact of the single event types in different parts of a year is evaluated.     

Maximum Likelihood Estimation of Life-Span Based on Censored and Passively Registered Historical Data

We consider the estimation of life length of people who were born in the seventeenth or eighteenth century in England. The data consist of a sequence of times of life events that is either ended by a time of death or is right-censored by an unobserved time of migration. We propose a semi parametric model for the data and use a maximum likelihood method to estimate the unknown parameters in this model. We prove the consistency of the maximum likelihood estimators and describe an algorithm to obtain the estimates numerically. We have applied the algorithm to data and the estimates found are presented.     

Capture-recapture: before and after EURING 2000

Capture-recapture studies and analyses have become an important tool for the study of bird populations. One reason for the rapid advancement in this area has been the EURING conferences where population biologists and statisticians meet to review recent progress, identify areas that require further work, and work collaborately to solve real world problems. In this paper, we forecast the needs for future research in this area and review the recent conference to try and identify what questions are yet unsolved. This EURING conference was dedicated to Dr George Seber who was the author of a number of key papers and whose name is synonymous with 'The estimation of animal abundance and related parameter' (Seber, 1982). He has retired from working in this field.     

Delineating bird populations using ring recoveries

We delineate bird populations using cluster analysis to group ringing sites based on pairwise comparisons of recoveries. Clustering provides a quantitative (but non-unique) grouping that can be used to examine the relationships of bird distributions at both local and regional geographic scales. Clustering is based on similarity matrices composed of pairwise comparisons of recovery distributions from ringing sites. We demonstrate the method using mallard ( Anas platyrhynchos ) ring recoveries to group ringing sites in south-central Canada, and discuss the possibilities for these analyses for non-hunted species with few recoveries.     

The Effect of Migration on a Similarity Index

Fewster and Buckland (2001 Fewster , R. M. , Buckland , S. T. ( 2001 ). Similarity indices for spatial ecological data . Biometrics 57 : 495 – 501 . [Google Scholar]) defined a similarity index between two communities by allowing changes between sites to reduce the influence of local discrepancies. The similarity index of Fewster and Buckland is calculated to attain the maximum similarity between two communities in the presence of migration. Instead of maximizing similarity, we propose random migration to measure the similarity of two communities with two types of stochastic migration. The similarity values based on the proposed methods can be treated as the expected value of similarity under migration. We use computer simulation and empirical examples to demonstrate our approach.     

Capture-recapture: Before and after EURING 2000

Capture-recapture studies and analyses have become an important tool for the study of bird populations. One reason for the rapid advancement in this area has been the EURING conferences where population biologists and statisticians meet to review recent progress, identify areas that require further work, and work collaborately to solve real world problems. In this paper, we forecast the needs for future research in this area and review the recent conference to try and identify what questions are yet unsolved. This EURING conference was dedicated to Dr George Seber who was the author of a number of key papers and whose name is synonymous with 'The estimation of animal abundance and related parameter' (Seber, 1982). He has retired from working in this field.     

Model Selection For A Family Of Discrete Failure Time Distributions

In recent years, a large number of new discrete distributions have appeared in the literature. However, flexible discrete models which, at the same time, allow for easy statistical inference, are still an exception. This paper makes a detailed analysis of a family of discrete failure time distributions which meets both requirements. It examines the maximum likelihood estimation of the unknown parameters and presents a goodness-of-fit test for this model. The test is used for the selection of an appropriate model for datasets of frequencies of the duration of atmospheric circulation patterns.     

The Role of Risk and Risk Aversion in an Individual's Migration Decision

Abstract

This paper proposes a simple, partial equilibrium model for studying an individual's migration decisions. It shows that an individual may choose to delay migration when the condition appears to be favorable, giving rise to the “waiting” behavior observed in the data. Using a closed-form solution, it also examines how the duration of the waiting is affected by a number of economic factors such as the risks associated with the wages in regions of origin and destination, the individual's attitude toward risk, etc.     

农民工职业流动的意愿及其影响因素研究——以杭州市建筑业为例

从个体微观的角度对杭州市建筑业农民工的工作情况进行实地调研,具体考察农民工的个人基本特征、家庭特征、工作特征及地区选择特征四类情况,并利用交叉分析和二元逻辑回归模型检验四类特征对农民工职业流动意愿的影响.研究结果显示,地区选择、从业时间、随同的家庭成员人数、工作中的人际关系等因素对农民工选择继续留在建筑业中工作具有显著正向作用.企业和政府等可以通过为农民工在城市创造稳定的生活和就业环境,推进农民工向城市迁移的进程,或可以加强对农民工的就业指导等多种途径来促使农民工维持稳定的职业,从而一定程度上缓解行业中“用工荒”难题.