1990年以来中国省级人口普查数据内部一致性检验及修正——以上海、四川和内蒙古为例

人口普查数据的质量评估和检验是人口学的重要议题。目前大部分的相关研究是检验全国人口普查数据,较少考察省级普查数据质量。文章选取地域特征鲜明,人口特征差异较大的三个省份:上海、四川和内蒙古,根据1990-2010年普查公布的人口结构与参数指标,运用二维死亡模型、队列存活法和反向预测法模拟三个省份以往20年的人口进程,以此检验与修正三个省份三次人口普查数据。研究发现,上海普查数据低估了1990和2000年的死亡率,高估了2010年的死亡率;四川死亡率的高估与生育率的低估较为突出;内蒙古的生育率低估和人口漏报较为明显。死亡率偏差主要是45岁以下的高估和60岁及以上的低估。生育数据存在一定程度的低估,2000年上海的普查生育率比实际生育率低估约0.015,四川两次普查分别低估0.35和0.44,1990年内蒙古普查生育率低估0.36。净迁移人口的年龄结构比迁移存量人口结构更年轻。

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.