Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

黑龙江（汉族）民歌衍变过程探析——对黑龙江、河北（汉族）民歌的比较

黑龙江汉族民歌的衍变历经了数百年的岁月沧桑，它与民族迁徙、人口变迁以及文化传统等存在着极为密切的联系。通过对黑龙江民歌与河北、山东、河南、山西民歌进行比较，表明黑龙江民歌与河北民歌之间存在着较近的亲缘关系。因此，文章试图在对较有代表性的几首黑龙江与河北民歌比较研究的基础上进一步探究黑龙江民歌衍变的影响因素。     

The Best and Worst Times of Life: Narratives and Assessments of Subjective Well-Being by Anamnestic Comparative Self Assessment (ACSA) in the Eastern Cape,South Africa

The Anamnestic Comparative Self Assessment (ACSA) measure of subjective well-being (SWB) aims to reduce the problems of cultural bias and relativity to external standards by allowing people to define the endpoints or ‘anchors’ of the measurement scale. In medical terminology anamnestic denotes ‘based on memory’. The ACSA uses subjects’ memories of the best and worst periods in their lives to define the anchors of the scale. They then assess their current quality of life relative to these personal anchors. The South African pilot study tested the match between self-assessment of SWB with ACSA and the conventional single-item measures of life satisfaction and happiness used in the South African Quality of Life Trends Study and analysed the narratives of the best and worst times of life. The quota sample of 46 consisted of 26 residents of Makana district in the Eastern Cape Province, South Africa, and 20 patients undergoing treatment in the local TB hospital. Mean SWB ratings with all three measures of life satisfaction, happiness and ACSA were between 5 and 6 on a 0–10-point scale. Ratings on all three scales were positively correlated. However, on ACSA the TB patients rated their current SWB 1.84 points lower than the community respondents, suggesting a greater sensitivity of this measure. It was observed that the starting points of the life stories produced by respondents to define the anchor periods for ACSA were related to their current assessment of SWB. A typology was developed that combined the starting point of the life stories with current SWB. The majority of community respondents matched the ‘Achiever’ type who scored positively on ACSA (i.e., above the mid-point of the scale) and whose life stories started with the worst period of their lives and proceeded to the best period. The TB patients were the only respondents to represent the ‘Survivor’ type whose morale had recovered after misfortune in life. ‘Survivors’ started their narratives with the best period in their lives, then moved to the worst (often health-related) one, and gave positive ACSA ratings. Based on the qualitative analysis of narratives, it is concluded that ACSA is a sensitive measurement instrument and therefore particularly useful for monitoring the effects of treatments and social interventions in longitudinal studies. However, further research is required to verify its cross-cultural validity.

Challenge of multi-level governance in developing countries and possible GIS applications

Of the three most prominent issues in governance discourse—the horizontal coordination of markets, government hierarchies and networks, global governance and multi-level governance—this paper argues that the last, developing and sustaining structures of multi-level governance, constitutes the most profound governance challenge for most developing countries.The paper identifies the major forces promoting change in favor of multi-level governance in developing countries. Using the growing literature and comparative national experiences (illustrated with the examples of India, the Philippines, Colombia and Nigeria), it highlights the key achievements of democratic decentralization as well as its nagging problems in these countries. Finally, the paper demonstrates the significance of these developments for democracy and development in these countries and shows some of the ways by which external partners—especially experts associated with the Geographic Information Systems for Developing Countries can assist and possibly benefit from this process.     

Merging information for semiparametric density estimation

Summary.  The density ratio model specifies that the likelihood ratio of m −1 probability density functions with respect to the m th is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established techniques to choose the smoothing parameter for the density estimators proposed.     

Estimation of Hazard, Density and Survivor Functions for Randomly Censored Data

Maximum likelihood estimation and goodness-of-fit techniques are used within a competing risks framework to obtain maximum likelihood estimates of hazard, density, and survivor functions for randomly right-censored variables. Goodness-of- fit techniques are used to fit distributions to the crude lifetimes, which are used to obtain an estimate of the hazard function, which, in turn, is used to construct the survivor and density functions of the net lifetime of the variable of interest. If only one of the crude lifetimes can be adequately characterized by a parametric model, then semi-parametric estimates may be obtained using a maximum likelihood estimate of one crude lifetime and the empirical distribution function of the other. Simulation studies show that the survivor function estimates from crude lifetimes compare favourably with those given by the product-limit estimator when crude lifetimes are chosen correctly. Other advantages are discussed.     

GPS接收机P(Y)直捕方法研究

GPS接收机P(Y)直捕算法是基于存储的滑动相关搜索,并利用FFT将时频二维联合搜索变成时域一维搜索。同时,考虑位同步点已知的条件,接收资源池只需存储一段接收序列,缩短了捕获时间且利于硬件实现。仿真结果表明,多普勒频移为±5kHz时,所带来的峰值衰减为2dB左右,本地信号与接收信号对齐时出现明显的相关峰,很好地实现了捕获功能;提出了用重叠分段补零FFT操作实现并行相关,成倍降低了捕获时间;提出了利用位同步信息,避免了码极性翻转可能引起的信噪比损耗;针对码相位漂移,提出了优化捕获策略,可在短时间内完成重捕。     

The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)

Conservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing