Electronic Journal of Applied Statistical Analysis最新文献_第6页

Student mobility in higher education: Sicilian outflow network and chain migrations 高等教育中的学生流动:西西里外流网络和链式迁移

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-12-15 DOI: 10.1285/I20705948V12N4P774

V. Genova, M. Tumminello, M. Enea, F. Aiello, M. Attanasio

{"title":"Student mobility in higher education: Sicilian outflow network and chain migrations","authors":"V. Genova, M. Tumminello, M. Enea, F. Aiello, M. Attanasio","doi":"10.1285/I20705948V12N4P774","DOIUrl":"https://doi.org/10.1285/I20705948V12N4P774","url":null,"abstract":"The Italian public universities are subsidised within a competitive framework that awards excellence, efficiency, and the capacity of universities to attract students from Italian regions other than its own. However, repeated cuts to public spending has increased the well-known Italian North-South divide. The most important student mobility (SM) flow is from the Southern to the Central-Northern regions--a phenomenon that has been magnified by an increasing number of outgoing students from Sicily over the last decade. In this paper, we rely upon micro-data of university enrolment and students' personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Indeed, our main goal is to eventually reveal the existence of chain migrations, through which students from a particular geographical area move towards a particular destination. We consider 38 clusters aggregating the 390 Sicilian municipalities, based on geographical proximity and socio-economic criteria. The data from each cohort is represented as a tripartite network with three sets of nodes, namely, clusters of Sicilian municipalities, students, and universities. The tripartite network is projected in a bipartite weighted network of clusters and universities, which is, then, filtered, in order to obtain a statistically validated bipartite network (SBVN). The SBVNs of the three cohorts suggest the existence and evolution of chain migration patterns over time, which are also gender specific.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"774-800"},"PeriodicalIF":0.7,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N4P774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44379178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

The role of employability in students during academic experience: a preliminary study through PLS-PM technique 就业能力在学生学术经历中的作用:PLS-PM技术的初步研究

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-12-15 DOI: 10.1285/I20705948V12N4P720

F. Signore, A. Catalano, E. Carlo, Andreina Madaro, Emanuela Ingusci

引用次数: 14

An investigation of mobility of Italian Ph. Doctors 意大利博士流动性调查

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-12-15 DOI: 10.1285/I20705948V12N4P748

Gabriele Ruiu, N. Fadda, A. Ezza, Massimo Esposito

{"title":"An investigation of mobility of Italian Ph. Doctors","authors":"Gabriele Ruiu, N. Fadda, A. Ezza, Massimo Esposito","doi":"10.1285/I20705948V12N4P748","DOIUrl":"https://doi.org/10.1285/I20705948V12N4P748","url":null,"abstract":"Migration is a permanent phenomenon rooted in history and recently involves high-skilled workers (HSWs). Among them, a crucial role is played by PhDs. HSWs face the risk to not find a job matching their skills and they can opt to accept to be overeducated for the job or move to another country or region. Mobility of HSW can be interpreted as a positive issue that can help to match jobs and skills. However, the emergence of a clear path between areas of countries or regions (e.g. from south to north Italy) highlights the risks of a drain of human capital from areas with low development to more developed ones. In this paper, we focus on a category of HSWs who have been almost neglected by the literature, the PhDs. The aim of the paper is to shed light on the mobility pattern of Italian PhDs, paying attention to PhDs from Southern Italy. This aim will be pursued by using microdata from the 2014 ISTAT Survey on the professional conditions of Italian PhDs at 4 and 6 years after the end of their studies.This work highlights that Southern PhDs have higher probability to move to other area of the countries, while Northern PhDs seem to prefer to move abroad thus confirming previous studies which identified a similar pattern for graduates. While the Northern part of the country compensate the drain of human capital with the mobility from the other part of Italy, the Southern face a relevant drain of ‘talents’.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"748-773"},"PeriodicalIF":0.7,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N4P748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41359635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Forecasting an explosive time series 预测爆炸性的时间序列

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P674

K. SureshChandra, S. Prabhakaran

{"title":"Forecasting an explosive time series","authors":"K. SureshChandra, S. Prabhakaran","doi":"10.1285/I20705948V12N3P674","DOIUrl":"https://doi.org/10.1285/I20705948V12N3P674","url":null,"abstract":"Forecasting is an important exercise in Time series analysis. For a statio-nary time series, there are theoretically strong forecasting methods which canprovide most accurate forecasts for the future (Karlin and Taylor (1975)).For most non stationary time series Box Jenkins methodology is a usefulforecasting technique. Essentially, the Box Jenkins methodology assumesthat any non stationarity time series can be conveniently modeled as anAutoregressive Intregrated Moving Averages (ARIMA) model with sucientnumber of unit roots in the linear stochastic dierence equation generatingthe time series. The non stationarity in such time series is then removed bysuccessively dierencing of the series until one obtains a stationary series,for which optimal forecasts can be computed. The forecasts for the originalseries are then computed by `inverting' the dierence operators that wereused ( Makridakis et al. (1998)) on the forecasts computed for the statio-nary series. The main objective of this study is to demonstrate that the BoxJenkins methodology is not useful, especially in large time series, when thenon stationarity in the time series is due to `explosive' roots. An alternativemethod is proposed in such a situation and its performance is assessed bothon a simulated as well as on a real life data.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"674-690"},"PeriodicalIF":0.7,"publicationDate":"2019-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N3P674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42089196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linear Systematic Sampling with Unequal Sampling Intervals in the Presence of Linear Trend 存在线性趋势时具有不等采样间隔的线性系统采样

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P705

J. Subramani

引用次数: 0

Inference on P(X less than Y) in bivariate Lomax model 二元Lomax模型中P（X小于Y）的推断

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P619

Rola M. Musleh, Amal Helu, H. Samawi

引用次数: 0

Voting-based Approach in Consensus Clustering through q-fold cross-validation 基于投票的q-fold交叉验证共识聚类方法

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P657

Norin Rahayu Shamsuddin, N. Mahat

引用次数: 0

Counting the unmarked: Estimating animal population using count data 计数未标记:使用计数数据估计动物种群

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P604

S. Gubbi, Srikanth R. Seshadri, V. Kumara, K. SureshChandra

{"title":"Counting the unmarked: Estimating animal population using count data","authors":"S. Gubbi, Srikanth R. Seshadri, V. Kumara, K. SureshChandra","doi":"10.1285/I20705948V12N3P604","DOIUrl":"https://doi.org/10.1285/I20705948V12N3P604","url":null,"abstract":"Understanding population parameters are important tools for wildlife management, and one of the key objectives of the ecological research. Motion sensor cameras are a widely used tool to estimate abundance and densities of species that are identifiable based on the natural markings on their bodies. Though camera trapping provides information such as count data, on species that are not individually identifiable, estimating population size using conventional capture-recapture methodologies is not possible hindering estimating population information of several wildlife species. However, recent methodologies help use camera trapping data to bridge this gap. Here we extend the model of Chandler and Royle (2013), with suitable modifications, and used camera trap detection data to estimate abundance and density of eight wild-prey, and five domestic prey species of leopards ( Panthera pardus fusca ). In this context, a new procedure has been proposed, based on grouping of the count data, which is useful in cases of large encounters. The current model should apply widely to a range of other unmarked wildlife species such as dholes, lions, golden jackal, Indian fox, ratel, to name a few, that could help understand prey-predator relationships, competition, trophic interactions, species interactions and other similar ecological questions. The methodology could also reduce costs, and maximise the utilisation of existing camera trapping data. The methodology helps understanding population parameters of several endangered, unmarked species to draw up conservation strategies whose estimates are currently largely based on educational guess.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"604-618"},"PeriodicalIF":0.7,"publicationDate":"2019-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N3P604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42035495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Application of Transformed Distribution: Length of Stay in Hospitals 转化分布在医院住院时间中的应用

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P691

S. Harini, M. Subbiah, M. R. Srinivasan, M. Gallo

引用次数: 0

A Statistical Model for the Self-evaluation of Teacher Satisfaction in School Management: a Study in the Italian Secondary School 学校管理中教师满意度自我评价的统计模型——以意大利中学为例

IF 0.7

Electronic Journal of Applied Statistical Analysis Pub Date : 2019-11-20 DOI: 10.1285/I20705948V12N3P637

P. Sarnacchiaro, S. Scippacercola, Pasqualina Malafronte

引用次数: 5