Heap : a command for estimating discrete outcome variable models in the presence of heaping at known points

Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gammaunobserved heterogeneity, allowing for fixed and individual-specific censoring and different-sized heap points. The second command, heapop, extends the framework to ordered choice outcomes, subject to heaping. We also provide suitable specification tests

Three essays on econometrics and economics of education.

This dissertation is a collection of three independent essays on econometrics and economics of education. The first chapter investigates how the Research Excellence Framework (REF) perceives the quality of economics journals. Exploiting aggregate information available in the published REF dataset, we propose a novel algorithm within an ordered probit framework that provides an effective recovery of underlying disaggregated outcomes (i.e. individual submission). The estimated results can be viewed as a directory for predicting the perception of journal quality for the REF 2014 exercise. In the second chapter, I suggest a new matching methodology—the Dirichlet process (DP) matching—that has several important advantages compared to conventional matching methods, including the balancing property, a more efficient ATT estimator and a credible confidence band. I describe the DP matching as a story of the “Chinese restaurant with invited guests”. In the third chapter, we exploit a quasi-natural experiment, namely the PelCa program in Ecuador, and study its consequences on mothers’ empowerment and children’s early development. We find optimistic evidence that the program helped mothers rear their children in a more learning conductive environment, resulting in positive effects on children outcomes and greater empowerment in mothers at home and in the community

Occurrence of False Positive Results for the Detection of Carbapenemases in Carbapenemase-Negative Escherichia coli and Klebsiella pneumoniae Isolates

Adequate detection of the production of carbapenemase in Enterobacteriaceae isolates is crucial for infection control measures and the appropriate choice of antimicrobial therapy. In this study, we investigated the frequency of false positive results for the detection of carbapenemases in carbapenemase-negative Escherichia coli and Klebsiella pneumoniae clinical isolates by the modified Hodge test (MHT). Three hundred and one E. coli and K. pneumoniae clinical isolates were investigated. All produced extended spectrum β-lactamases (ESBLs) but were susceptible to carbapenems. Antimicrobial susceptibility testing was performed by the disk diffusion and agar dilution methods. The MHT was performed using the standard inoculum of test organisms recommended by the CLSI. Genes that encoded ESBLs and carbapenemases were identified by PCR and DNA sequencing. Among the 301 clinical isolates, none of the isolates conformed to the criteria for carbapenemase screening recommended by the CLSI. The susceptibility rates for imipenem, meropenem, and ertapenem all were 100.0%, 100.0%, and 100.0%, respectively. Of the 301 E. coli and K. pneumoniae isolates, none produced carbapenemase. The MHT gave a positive result for 3.3% (10/301) of the isolates. False positive results can occur when the MHT is used to detect carbapenemase in ESBL-producing isolates and clinical laboratories must be aware of this fact

Empowering mothers and enhancing early childhood investment : effect on adults outcomes and children cognitive and non-cognitive skills

Empowering women and enhancing children’s early development are two important goals often pursued via independent policy initiatives. In this paper we study a unique approach that targets both at the same time. AVSI, an Italian NGO, provides family advisor-guided parent training sessions in a poor neighborhood of Quito, Ecuador. We find that the program empowered women in various dimensions, including higher likelihood of employment in the formal-sector and wages. Treated mothers have a greater role in intra-household decisions, especially on children’s education and discipline and increase parental inputs into their children’s development. Treated children improve their cognitive and non-cognitive skills

heap: A command for tting discrete outcome variable models in the presence of heaping at known points

Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gammaunobserved heterogeneity, allowing for fixed and individual-specific censoring and dierent-sized heap points. The second command, heapop, extends the framework to ordered choice outcomes, subject to heaping. We also provide suitable specification tests

heap: A command for tting discrete outcome variable models in the presence of heaping at known points

Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gammaunobserved heterogeneity, allowing for fixed and individual-specific censoring and dierent-sized heap points. The second command, heapop, extends the framework to ordered choice outcomes, subject to heaping. We also provide suitable specification tests

Two new species of the genus Macrothele Ausserer, 1871 (Araneae, Macrothelidae) from China

The family Macrothelidae Simon, 1892 belongs to the infraorder Mygalomorphae, currently contains two genera and 47 described species, from South Europe, South, and East Southeast Asia, Central, West, and North Africa.Two new species of the funnel-web spider genus Macrothele Ausserer, 1871 from Yunnan Province, China are described: Macrothele washanensis Wu & Yang, sp. n. (♂♀), and M. wuliangensis Wu & Yang, sp. n. (♂♀). Detailed descriptions, diagnostic illustrations and distribution map are provided. All specimens are deposited in the Institute of Entomoceutics Research, Dali University (DUIER)

Two new species of genus Macrothele (Mygalomorphae, Macrothelidae) from China

The family Macrothelidae Simon, 1892 belongs to the infraorder Mygalomorphae, currently contain two genera and 47 described species from South Europe, Southeast Asia, and Africa.Two new species of the funnel-web spider genus Macrothele Ausserer, 1871 from Yunnan Province, China are described: Macrothele washanensis Wu & Yang, sp. n. (♂♀), and M. wuliangensis Wu & Yang, sp. n. (♂♀). Detailed descriptions, diagnostic illustrations and distribution map are provided. Type specimens are deposited in the Institute of Entomoceutics Research, Dali University (DUIER)

A Topic Representation Model for Online Social Networks Based on Hybrid Human-Artificial Intelligence

With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human-AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user's modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog's actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model.This work was supported in part by NSFC under Grant 61972106, Grant U1636215, Grant 61871140, Grant 61572153, and Grant 61872420, in part by the National Key research and Development Plan under Grant 2019QY1406 and Grant 2018YFB0803504, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010136003 and Grant 2019B01013700