Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model

Assessing regional population compositions is an important task in many research fields. Small area estimation with generalized linear mixed models marks a powerful tool for this purpose. However, the method has limitations in practice. When the data are subject to measurement errors, small area models produce inefficient or biased results since they cannot account for data uncertainty. This is particularly problematic for composition prediction, since generalized linear mixed models often rely on approximate likelihood inference. Obtained predictions are not reliable. We propose a robust multivariate Fay–Herriot model to solve these issues. It combines compositional data analysis with robust optimization theory. The nonlinear estimation of compositions is restated as a linear problem through isometric logratio transformations. Robust model parameter estimation is performed via penalized maximum likelihood. A robust best predictor is derived. Simulations are conducted to demonstrate the effectiveness of the approach. An application to alcohol consumption in Germany is provided.     

A Comparison of Methods for Poverty Estimation in Developing Countries

Small area estimation is a widely used indirect estimation technique for micro‐level geographic profiling. Three unit level small area estimation techniques—the ELL or World Bank method, empirical best prediction (EBP) and M‐quantile (MQ) — can estimate micro‐level Foster, Greer, & Thorbecke (FGT) indicators: poverty incidence, gap and severity using both unit level survey and census data. However, they use different assumptions. The effects of using model‐based unit level census data reconstructed from cross‐tabulations and having no cluster level contextual variables for models are discussed, as are effects of small area and cluster level heterogeneity. A simulation‐based comparison of ELL, EBP and MQ uses a model‐based reconstruction of 2000/2001 data from Bangladesh and compares bias and mean square error. A three‐level ELL method is applied for comparison with the standard two‐level ELL that lacks a small area level component. An important finding is that the larger number of small areas for which ELL has been able to produce sufficiently accurate estimates in comparison with EBP and MQ has been driven more by the type of census data available or utilised than by the model per se.     

Comparing Inference Methods for Non‐probability Samples

Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data sources generally cover an unknown part of the population of interest. Simply assuming that analyses made on these data are applicable to larger populations is wrong. The mere volume of data provides no guarantee for valid inference. Tackling this problem with methods originally developed for probability sampling is possible but shown here to be limited. A wider range of model‐based predictive inference methods proposed in the literature are reviewed and evaluated in a simulation study using real‐world data on annual mileages by vehicles. We propose to extend this predictive inference framework with machine learning methods for inference from samples that are generated through mechanisms other than random sampling from a target population. Describing economies and societies using sensor data, internet search data, social media and voluntary opt‐in panels is cost‐effective and timely compared with traditional surveys but requires an extended inference framework as proposed in this article.     

一种新的极高精度的T_IIN频率估计内插算法

针对多普勒条件下接收端复信号的频率估计难的问题，研究了一种基于离散傅里叶变换与迭代频率估计的内插综合算法。区别于经典的内插算法，新算法在迭代频率内插算法基础上充分利用复数快速傅里叶变换结果的实虚部值，并通过最大峰值频谱和相邻两侧谱线以极高精度内插估计出复信号的频率参数。仿真结果分析表明,在二次迭代条件下信噪比为-10 dB时，该算法估计均方根误差仍能逼近克拉美-罗限的1.002 1倍。该算法在同等条件下比经典的Rife、Quinn和IIN算法具有更高的准确性、稳定性和可靠性。     

星载AIS信号的频偏估计

星载自动识别系统（AIS）中信号存在严重的多普勒频偏，影响信号的正确解调，而传统的基于快速傅里叶变换（FFT）的频偏估计算法其估计范围无法满足需要。为此，提出了一种改进的自相关-FFT频偏估计算法，通过构造辅助函数以确定信号频偏的正负号，在此基础上对基于FFT的频偏估计算法进行改进，从而扩大频偏估计范围，实现对大的多普勒频移的估计。仿真和实验结果均表明，改进算法的频偏估计范围扩大为原来的2倍，具有很高的估计精度，十分接近修正后的克拉美罗界，且算法简单、易于实现。     

Preaching democracy: The second Vatican council and the third wave

We use variation in religious doctrine produced by the unexpected Second Vatican Council (1962–65) to investigate the impact of religion on democratization. The Council, which transformed the Catholic Church from defender of the ancien régime into a leading apostle of religious freedom, human rights and democracy, represents the most significant example of institutionalized religious change since the Protestant Reformation. We adopt a difference-in-difference approach to estimate the Council's impact on democracy. Furthermore, we provide historical narratives on how the post-conciliar Catholic Church influenced the democratization process in different national contexts. Our research substantiates that the Church played a decisive role in third wave democratization.     

切换ARMAX系统切换时刻检测算法

辨识切换线性系统的主要难点在于参数估计问题与子系统划分问题耦合。针对该问题，利用卡尔曼滤波与递推扩展最小二乘的联系，证明当所辨识的带外源输入的自回归滑动平均（ARMAX）切换系统在满足严正实条件下，当且仅当输入输出数据来源于同一个子ARMA系统时所构造的新息序列具有白色性。基于此,提出了一种切换ARMAX系统切换时刻检测算法。仿真计算结果验证了所提算法的有效性。     

Empirical decomposition of customer responses to discount coupons in online FMCG retailing

This study considers the efficacy of ‘general’ discount coupons: i.e. those whose discounts apply storewide. We decompose purchase behavior into three effects: spending, basket size, and inter-purchase time. The effects of dollar-off vs. percentage-off coupons are examined for 3,827 customers who had redeemed both types of coupon framings in an Australian online fast-moving consumer goods (FMCG) retail context. Results from Bayesian simultaneous equation modelling confirm that compared to dollar-off coupons, percentage-off general coupons induce customers to both spend more and shop more items. Moreover, by capturing consumer heterogeneity the customer base can be segmented into several clusters based on their reaction to coupons.     

Unveiling the objectives of central banks: Tales of four Latin American countries

We estimate the stabilization objectives of four Latin American countries that have implemented a flexible inflation targeting regime recently: Brazil, Chile, Colombia and Peru. In doing so, we develop a New Keynesian dynamic stochastic general equilibrium model for these economies and estimate their structural parameters through Bayesian methods. To infer the stabilization objectives in each country, we assume that central banks set monetary policy optimally. Our main results highlight that the central banks in these four countries have a high preference for stabilizing inflation, but do not have the systematic objective of stabilizing the exchange rate. This result is robust to assuming either commitment or discretion in the optimal policy. Also, in contrast to the case of commitment, assuming discretion in the optimal monetary policy increases the preference for interest rate smoothing, making it comparable to a preference for inflation stabilization. Finally, except for the case of Peru, the monetary policy under discretion has a better empirical fit in these countries than the one under commitment.