Explore the vast range of titles available.

Statistical models using historical data on crop yields and weather to calibrate rela- tively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale （e.g. global scale, national scale, provincial scale and county scale）. Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.

This study is focused on indexes for the rice chilling injury in Heilongjiang Province during 1960-2009. Firstly, we compared a new derived climate data weighted by rice planting density with the traditional method, and found that the new one is more reasonable to assess the impact of climate change on crop yields. Considering the frequency and intensity of rice chilling in the province, secondly, chilling indexes defined by meteorological, national and international levels were assessed. The result showed that the meteorological standards were suitable for the delayed-type injury, while the international one, so-called sum of Grow- ing Degree Day below threshold （GDDn.）, characterized best the sterile-type chilling injury for rice. The explanation ability of the rice yield time series model including both injury types as two independent variables reached approximately 92% （p 〈 0.05）. Finally, we concluded that the contribution rates of human and weather factors to rice yields are about 87.2% and 12.8% respectively, and as light increasing trend for sterile-type chilling injury was found during heading to flowing period in recent years, indicating a high chilling risk for rice planting in Heilongjiang Province in the future global warming.

This paper presents a method of constructing a mixed graph which can be used to analyze the causality for multivariate time series. We construct a partial correlation graph at first which is an undirected graph. For every undirected edge in the partial correlation graph, the measures of linear feedback between two time series can help us decide its direction, then we obtain the mixed graph. Using this method, we construct a mixed graph for futures sugar prices in Zhengzhou (ZF), spot sugar prices in Zhengzhou (ZS) and futures sugar prices in New York (NF). The result shows that there is a bi-directional causality between ZF and ZS, an unidirectional causality from NF to ZF, but no causality between NF and ZS.

This paper attempts to investigate comprehensively, a ＂U＂-shaped relationship between income inequality and crime rates in China after building a cost-benefit analysis model, by using time series data from 1981-2012 and panel data from 1999-2012. The empirical results show that： firstly, in the time series model, the U-shaped relationships between inequality and the total crime rate and rates of various crimes except from smuggling, are very significant in the period of 1981-2012, secondly, the panel threshold models show that inequality and crime tend to be correlated positively with each other during 1999-2012, because the inequality level during this period is much higher than the turning points of inequality estimated in the time series models, although three regions with different development levels are located in different parts of a U-shaped curve between inequality and crime.

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated. First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean（and the true density function） are derived. Secondly,the corresponding strong convergence rates are investigated. It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d. density models are also optimal for these processes.

Traditional studies on integrated statistical process control and engineering process control （SPC-EPC） are based on linear autoregressive integrated moving average （ARIMA） time series models to describe the dynamic noise of the system. However, linear models sometimes are unable to model complex nonlinear autocorrelation. To solve this problem, this paper presents an integrated SPC-EPC method based on smooth transition autoregressive （STAR） time series model, and builds a minimum mean squared error （MMSE） controller as well as an integrated SPC-EPC control system. The performance of this method for checking the trend and sustained shift is analyzed. The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

In this paper, we study the problem of a variety of nonlinear time series model Xn+1 = TZn+1(X(n), …, X(n − Zn+1), en+1(Zn+1)) in which Zn is a Markov chain with finite state space, and for every state i of the Markov chain, en(i) is a sequence of independent and identically distributed random variables. Also, the limit behavior of the sequence Xn defined by the above model is investigated. Some new novel results on the underlying models are presented.

目標：肺結核對世界而言是重大的健康威脅，但是世界各國在千禧年以來防治肺結核的成效表現不一，其中還有退步者。在解釋國家公共衛生議題的成效差異時，近年來被提出的一個解釋，就是國家對自然資源的依賴程度，但是學者對自然資源產生的效果並沒有共識，對肺結核的討論也付之闕如。本研究期望藉由探討自然資源依賴程度與肺結核防治成效之間的關係，補足這部分的研究缺口。方法：本文以時間序列橫斷面方法中的固定效果模型，檢驗2000-2016年之間97至135個不等的國家或政治實體的肺結核與自然資源依賴程度的相關性，並控制人均GDP、政府效率、15-49歲的人口中HIV發生率、0-14歲的人口佔總人口的百分比、5歲以下兒童死亡率（每千例活產兒）、人口密度、都市化程度以及經常性醫療保健支出占GDP比重（%）等幾個解釋變數。結果：在2000-2016年之間，國家對自然資源的依賴程度愈高，肺結核的發生率以及死亡率就愈高，兩者的統計結果均至少達到0.01的顯著水準。結論：根據研究發現，要降低自然資源與肺結核之間的相關性，依賴自然資源程度較高的國家，可能可以藉由：(1)轉型經濟減少對自然資源的依賴；(2)以及（或是）強化對自然資源收益的管理；(3)改善礦區的工作與衛生狀況，來降低肺結核發生率或死亡率。

随着空间观测技术的高速发展,GPS已成为大地测量领域重要的观测技术手段之一。全球IGS基准站积累了近20余年的位置时间序列,为大地测量及地球动力学研究提供了丰富的基础数据。GPS坐标序列不仅包含构造信号,也包含地表环境负载以及未模型化的误差等干扰源的影响,降低了GPS解的精度与可靠性。分析GPS时间序列,尤其是坐标时间序列非线性变化,进一步深入系统地了解GPS非线性变化起源及其影响机制,

以呼伦贝尔草原核心区的新巴尔虎右旗、新巴尔虎左旗、陈巴尔虎旗和鄂温克族自治旗为主要研究区,基于MODISNDVI数据,利用像元二分模型反演得到植被覆盖度,并结合土地覆盖分类产品,构建2000—2014年研究区植被覆盖度时间序列。通过时间序列分析,从不同的时间和空间尺度分析草原植被覆盖度的变化规律;同时引入覆盖度异常变化点检测算法,并结合该地区同期气象数据,进一步探讨研究区植被覆盖度变化与气象因子之间的内在驱动力关系。结果表明,植被覆盖度在空间分布上主要表现为:从东往西依次递减,特别是研究区西南部,覆盖度最低;15年来研究区植被年际变化总体上呈现前10年下降、后5年缓慢上升的趋势。对植被覆盖度的异常变化进行分析,结果显示:返青期和枯萎期覆盖度的剧烈变化与温度的相关性较大,生长旺季内(7—8)月覆盖度的剧烈变化主要与降水量有关。