dataesr/scientific-paragraphs-categorization huggingface dataset:
Grid on α The inference presented above works for the rescaled BM, when the parameter α is supposed to be known. In practice, this parameter needs to be estimated. One simple way to do that is to use a grid on α. For each value on the grid, one can find an associated estimator, and then find the maximum likelihood estimator of the parameters by taking the best likelihood, for each number of shifts K. For instance, we plot below (Fig. 16 ) the likelihood profile in K for 30 α values on a grid, for the New World Monkey dataset  (Aristide et al. 2016). This grid of α values can be provided by the user, depending on some a priori knowledge she might have of the problem at hand. If no grid is provided, one is automatically computed, with n α values, evenly spaced on a log scale ranging between α min and α max.
This rise and associated preeminence of applied studies have been intensified by the availability of databases and 'big data' -features extraction from unstructured very large databases. Some mainstream studies may now claim that to be 'a-theoretical' constitutes 'real' and 'credible' science that avoids the 'ideological a-prioris' of theories (such as, among others, heterodox assumptions or qualitative analytic methods). In this perspective, the availability of a database is the start of an analysis, and good mainstream economics is the search for some correlation that statistical techniques can extract from that database, the latter being viewed as the essence of scientifically presented empirics. For example, the availability of satellite data on earth lights has generated a vast literature correlating them with growth, income, inequality, institutions, crop production, etc.
Each iterative expert input was integrated into the proposed taxonomy and either offered support for other experts' opinions or provided the basis for another quadrant in the matrix. It is to be noted that only two rounds of questions were used in this research, rather than three rounds as sometimes recommended  (Czinkota & Ronkainen, 1997; Lee & King, 2008). Indeed, the method must be adapted to the issue being examined, and to the availability of the experts solicited. The data collected from the experts in the two successive rounds of interviews provided a satisfactory level of saturation and therefore sufficient validation. The inclusion of the two institutional experts in the panel provided a complementary cross-sectoral and historical perspective of FDI issues in Japan. Their contribution further validated the assessments of corporate experts.
Data and preliminary evidence The data sources used in this paper were collected primarily from the World Bank Development Indicators (WDI), the Organization for Economic Cooperation and Development (OECD) National Accounts data, the International Monetary Fund (IMF), the International Financial Statistics and Balance of Payments databases (IFSBPD), the International Debt Statistics (IDS), the UN -National Bureau of Economic Research (NBER) dataset, the COMTRADE bilateral trade flow data, the World Integrated Trade Solution database (WITS), the International Comparison Progra m database (ICPD), Government Finance Statistics Yearbooks, and other available sources. Appendix A1 indicates descriptively all the variables used in our analysis along with brief definitions and the data sources. Due to the lack of available data for some variables, especially for Asian countries, the estimation uses a dataset of only 52 countries 4 (16 countries from the East Asia & Pacific region, 7 countries from South Asia, 2 countries from North America, and 27 countries from Europe) or 22 Asian countries and 30 Western countries between 1988 and 2014.
The structure of inequality and globalization over the last few decades Without looking in depth at real databases, it might be thought that the advanced countries like the EU member states and Anglo-Saxon countries, known as the most liberal economies, would be characterized by much higher degrees of trade openness and FDI and especially by higher levels of sector-specific export specialization in manufacturing and high-technology sectors and, consequently higher levels of inequality as predicted by traditional international trade theories. However, the trends in the economic variables over the last few decades might cause surprise.
Moreover, continental European countries have not experienced any significant increase in inequality and, at the same time, in some countries like France, inequality is decreasing. For Asian economies, on the other hand, inequality is very different, based on the degree of economic development of each country such as the Asian dragons, the four Asian tigers (Thailand, Malaysia, Indonesia and the Philippines), the other ASEAN 5 countries, China, India, etc. ). Yet, in general, these countries have experienced higher inequality than the OECD countries In the database we used for this estimation, the lowest value of the Gini coefficient is 19. 4 while the highest value is 63. 26, with an average value of around 35. 7 from 1988-2014 (please see Appendix A6 for details). The Gini coefficient does indeed differ considerably between sub -groups of countries.
Sectoral export diversification Export diversification is measured by the Theil index, calculated by the IMF. The data cover 187 countries from 1962-2010. In our database, the data come from the UN-NBER dataset, which harmonizes COMTRADE bilateral trade flow data at the 4-digit Standard International Trade Classification (SITC) Rev. 1 level. Based on the results in Appendix A6, the lowest value is 1. 14 while the highest value is 5. 37, and the average value is 2. 51 for the period from 1988 to 2010. The export diversification index differs for the different sub-groups of countries. We observed that the higher the level of economic development, the lower a country's export diversification. In other words, richer countries tend to concentrate on producing exports. The pattern of the relationship between sectoral export specialization and GDP per capita can be described as a 'Ushaped curve'  (Becuwe, Blancheton, and Meissner 2018).
Manufacturing specialization We used the Balassa revealed comparative advantage index of manufactured goods, taken from WITS, to measure manufacturing specialization. 6 It covers every country in the world from 1988 to 2014. The rationale for choosing this index is that it parallels Dingel's (2016) theoretical and empirical work on the determinants of quality specialization, which employs micro -data on US manufacturing sectors and factor inputs to quantify the roles of the two mechanisms in quality specialization in US cities. The results of the estimation detailed in Appendix A6 show that the lowest value of the index is 0. 03 while the highest value is 1. 47, and its average value is 0. 95 for the period 1988 to 2010. Looking at different country profiles, we observed that high-income Asian countries have the highest value of the specialization index, followed by EU member states whose index is greater than 1.
However, we also found the lowest minimum value in high-income Asian countries. Notably, the Anglo-Saxon countries have the lowest index. Based on standard deviation (see Appendix A7), there is higher disparity in the datasets for Asian countries and Anglo -Saxon countries than in that for EU member states. Since 1988, although there has been a very different profile for each sub-group of countries, the index has varied between 0. 8 and 1. 1 and it seems to have been moving towards a stable position since the 1990s. The value of the index has remained above the average for the 52 countries only in the high-income Asian countries and EU member states. In contrast, the Anglo-Saxon countries continue to have the lowest index among all the groups of countries.
Specification function: globalization and income inequality The theoretical basis explaining the effect of globalization on income inequality is unclear. Moreover, the effects at the disaggregated level of sectoral export diversification and manufacturing specialization should be studied empirically. Therefore, this paper conducts panel estimations of the effects on these variables. Ideally, the panel estimations should allow us to take into consideration both unobserved time-invariant and individual-invariant factors. With a panel dataset of small time-series dimensions (T = 26 years) and large cross-sectional dimensions (N = 52 countries), this paper applies the system GMM estimators to obtain efficiency and consistency. The econometric model is structured as follows  (Greene 2012) : gini 𝑖𝑡 = ∝ + 𝜗gini 𝑖𝑡-1 + 𝛽𝑇Glob 𝑖𝑡 + 𝜌𝐷Glob 𝑖𝑡 + ∅𝑊 𝑖𝑡 + ∑ 𝜙 𝑇 𝑦 𝑡-𝑇 + 𝑢 𝑖 + 𝜀 𝑖𝑡 𝑇 𝑇=1 for 𝑖 = 1, 2, … , 𝑁 and 𝑡 = 1, 2, … , 𝑇 ( 1) Where, - and  represent country and time respectively while ∝ represents the intercept.
For example, just looking at one group of countries, there is huge inequality among the members of the ASEAN. The GDP per capita of Singapore is nearly 37 times higher than the average GDP per capita of the CLMV (Cambodia, Laos, Myanmar and Vietnam) countries. 12 This has also required Asian countries to rethink their model of social and economic development that used to focus mainly on economic growth alone, ignoring the majority of people who are still vulnerable due to living in pover ty. In this paper, the estimated results, examining dynamic panel data models, provide significant insight to better understand the effect of less-explored areas of sectoral export diversification and manufacturing specialization on income inequality in both Asian and Western countries. However, there remain many crucial questions for further research.
First, the Gini coefficient is always the first choice when measuring income inequality; however, if the study wishes to expand to more developing countries, like those in Asia, the large number of missing values becomes of greater concern. Although we could deal with this problem by using reliable statistical methods, it might still produce results that are unrepresentative of reality. That is why we might consider using 'The World Wealth and Income Database (WID. world)', first developed by  Piketty and Zucman (2013, 2014) , which is currently available in many more countries. Second, while using the Balassa revealed comparative advantage index of manufactured goods, we have not taken a closer look at 22 sub-categories, especially in quality-and high-technology-intensive sectors. Studying a high level of aggregation of this indicator may possibly give more fragile results.
Third, we could not find any statistically significant coefficients for Anglo-Saxon countries. We suspected that this is because the database contains a small number of observations. Fourth, we might consider comparing the effects in South American countries, whose level of economic development is parallel to that of the Asian economies. Last, but not least, sectoral export specialization in agricultural and service sectors in developing countries should be taken into consideration since agricultural sectors play significant role in many developing countries in Asia and service sectors are increasing in significance.
Acknowledgments. We thank  Martin Schweizer , two anonymous referees and the associate editor for their comments that have resulted in a significant improvement of the manuscript. Alexandros Saplaouras gratefully acknowledges the financial support from the  DFG   Research Training Group 1845  " Stochastic Analysis with Applications in Biology, Finance and Physics". Dylan Possamaï gratefully acknowledges the financial support of the ANR  project  Pacman ,  ANR-16-CE05-0027. Moreover, all authors gratefully acknowledge the financial support from the  Procope  project "Financial markets in transition: mathematical models and challenges".
Data and population The present data were drawn from a large cross-sectional survey conducted in four regions of France: Normandy, Ile-de-France, Lorraine and Rhône-Alpes representing one third of the French population and largely diverse regions. Trained interviewers, using a computer-assisted telephone interviewing system, collected data between April and June 2005. In each region, participants were selected using a two-stage procedure. First, 59,836 households with landline numbers were randomly contacted. Second, one person was randomly selected within each household according to a method proposed by Kish  [18]. Landline telephone numbers listed in the directory for each region were randomly chosen. The last digit of each number was then replaced with a randomly chosen number so as to include both listed and unlisted numbers.
In addition to this sample, a mobile phone-only sample was collected in order to reach persons who were not equipped with a landline. Once the data were pooled, the final sample included 22,138 participants with an overall response rate of 62. 7%. Exclusion criteria included being a non-French speaker, being a minor, being unable to answer the phone or complete the interview (i. e. the person suffered from deafness, did not answer the questions or answered inconsistently, was intoxicated, or suffered from a physical illness that prevented him or her from talking for a long period of time). Participants were given a complete description of the study and provided informed consent. The French ethics committee of the National Data Protection Authority (CNIL) approved recruitment and consent procedures.
Several limitations should be acknowledged. First, as the data were collected in 2005, it is possible that more recent efforts in diagnosing and treating back pain and other diseases may have affected the prevalence and health-related consequences of chronic back pain. Though the demography has also changed, we found that the  proportion of men vs. women is still 48:52 according to 2016 French census (INSEE). The second limitation resides in our decision to categorize neck pain, back pain and chronic back pain as one group when the complexities of these conditions might differ from one another and be associated with different outcomes. Third, the conditions were self-reported without any professional or medical assessment potentially causing biased reporting. Fourth, the assessment of pain did not include information on the duration or the intensity of pain.
Search and selection strategy A systematic literature search was performed in the Pubmed database, of all articles published between January 2000 and May 2018. Eligible studies included original articles aiming to determine the MID of self-administered questionnaires in cancer, using distribution-and/or anchor based approaches. Only static questionnaires were considered, i. e. questionnaires that have a fixed number of questions answered by patient. It means, all patients will answer to the same questions in the same order. Accordingly, computer adaptive tests were not included in this review. Indeed, all studies using Item Response Theory models were not included since these types of models are very specific and results could not be directly comparable to studies using the summary score. All non-cancer studies were excluded as well as reviews and meta-analysis.
Data extraction Two reviewers (A. O. , C. T. ) independently screened first titles and abstracts and secondly full paper to identify relevant articles. Then, they independently extracted information from eligible studies using a predefined data extraction form (DEF). All discrepancies were resolved by mutual consensus. In case of disagreement, a third author (A. A. ) was consulted to reach a final consensus. This literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines  [18]  and the following details were extracted: -General items, namely, year of publication, number of patients, disease stage, type of study (randomized clinical trial, prospective cohort, or other), study location, international and multicenter study or not.
Using only one electronic database (PubMed searches) was the main limitation of this work. Unfortunately, due to lack of resource, we could not use other databases to perform this review. A risk of bias could thus be observed since other interesting papers may not be captured in this database. Hopefully, a manual research conducted to the same papers obtained via our algorithm in Pubmed. This review must be expanded in future studies to address all methods that have been used to determine the MID either if they are including or not including in distribution-based or anchor-based methods (i. e. minimal detectable change, Receiver operating characteristic (ROC) curve, Item Response Theory, etc. ). In light of these results, greater attention should be paid to the methodology in future studies investigating the MID of a given PRO questionnaire, in order to ensure reliable results.
Background: This article corresponds to a literature review and analyze how heterogeneity of treatment (HTE) is reported and addressed in cohort studies and to evaluate the use of the different measures to HTE analysis. Methods: prospective cohort studies, in English language, measuring the effect of a treatment (pharmacological, interventional, or other) published among 119 core clinical journals (defined by the National Library of Medicine) in the last 16 years were selected in the following data source: Medline. One reviewer randomly sampled journal articles with 1: 1 stratification by journal type: high impact journals (the New England Journal of Medicine, JAMA, LANCET, Annals of Internal Medicine, BMJ and Plos Medicine) and low impact journal (the remaining journals) to identify 150 eligible studies.
Data collection process We developed a data extraction sheet, pilot-tested it on ten randomly-selected included studies, and refined it accordingly. Two reviewers (MD, CS) extracted the relevant data from all included studies. Disagreements between the reviewers were resolved by consensus, and if necessary, consultation with an arbitrator (DB). Authors were not contacted for further information. Only the materials and methods section, results section, tables and figures were reviewed; the introduction and discussion sections were not read through; an exception was made for source of funding (see below). Reviewers also identified a pair-wise comparison of interest, using the following strategy. If there were only two groups, these groups were considered for the analysis. If there were more than two groups, the comparison that was clearly and explicitly defined as the primary comparison in the study report was considered only; if the primary comparison was not explicitly defined, we selected the comparison that reported the largest number of HTE analyses for the selected primary outcome.
Study selection A total of 150 prospective cohort studies, 75 in high impact and 75 in low impact journals, were identified for inclusion in the review after screening 2019 citations on PubMed (Fig. 1 ). The year 2000 was reached for the New England Journal of Medicine after 11 cohort studies were retrieved instead of 15 planned and 8 were added a excluding studies assessing only a form of treatment *to determinate p-value we used the Wilcoxon rank sum test for the analysis of continuous data and the chi square/fisher test for binary data to Plos Medicine. Eligible cohorts were identified in low impact journals without the need to correct for numbers.
Research methods The empirical data used in this paper were generated from a larger project that traced the development of the Hungarian mortgage market from its design in 1996 until 2014, the end of its deepest crisis. The archival material consisted of all parliamentary debates (referenced as  PD, 1997 PD, -2014)) , laws and government decrees  (L&D, 2000-2011)  related to mortgages, Reports on Financial Stability issued by the Central Bank of Hungary  (SR, 2000 (SR, -2014)) , periodic reports of the Hungarian Financial Services Authority  (PSZÁF, 2000 (PSZÁF, -2012)) , annual reports of the Hungarian Banking Association  (Bankszövetség, 2001 (Bankszövetség, -2011)) , and reports issued by the Parliamentary investigative committee on mortgage lending  (PIC, 2012). Complementing the documentary evidence, we carried out 40 expert interviews, including with key decision-makers in the Hungarian mortgage market.
Data The data employed in this study are composed of daily closing prices indices of PSI20 (Portugal), ISEQ (Ireland), MIB30 (Italy), ASE20 (Greece), IBEX35 (Spain) and SPEURO (S&P Europe 350; used as an EU stock market benchmark). All data sets encompass the interval from January 2, 2004 to December 31, 2011. However, the analysis is performed in two sub-intervals: the first (pre-crisis period) from January 2, 2004 to December 31, 2007 (1042 datapoints), and the second (crisis period) from January 2, 2008 to December 31, 2011 (1044 datapoints). The number of datapoints in each interval is practically the same to improve comparability. In order to cope with the different official holidays, we have adjusted the indices using the last closing price corresponding to each official holiday. Data were obtained from Yahoo, 1  except for the PSI20 and SPEURO stock market indices, which were taken from BolsaPT 2  and OnVista Group,  3 respectively.
[16]  and Benhmad  [9]. For this reason, the effective number of wavelet scales is in principle limited to five (J = 5). However, we have decided to analyze only the first four wavelet scales because, after applying the MODWT to a sub-sample (or "window") with 250 datapoints and trying to avoid the boundary wavelet coefficients, the number of datapoints is much smaller than 250 for scale D5. Thus, we have obtained N -w (where N = 1043 and w = 250, then, N -w = 793) windows, and therefore, correlation coefficients. Finally, we have introduced a new way of visualizing the RWWC. The code is available from the Corresponding Author upon request, and it will also be available in the next version of our R package W2CWM2C freely available from CRAN repository  [50].
The test of Hiemstra and Jones  [35]  is one of the most used nonlinear causality tests in economics and finance  5. However, it tends to over-reject the null hypothesis when the test is satisfied  [19, 20]. For this reason, Diks and Panchenko  [20]  proposed a new nonparametric and nonlinear Granger causality test to avoid the over-rejection. In this work, we use this later causality test applied to the two time intervals of study (pre-crisis and crisis periods), as implemented in the C program GCTtest, which is freely available online  6. The description of the test is presented in the following lines, and it is based on Diks and Panchenko  [20]  and Bekiros and Diks  [7]. Testing Granger causality between two time series X t and Y t is based on the null hypothesis that X t does not contain additional information about Y t+1  [7, 20].
Competitiveness (grant TM2016-74931). M. B. N. (RYC-2013-13628) and S. H. F. (RYC-2012-12167) acknowledge financial support from the Ramón y Cajal Research Fellowship of the Ministry of Economy and Competitiveness of Spain. We would like to show our gratitude to the anonymous reviewers and the main Editor Prof. Stanley whose comments and suggestions have been very helpful in improving this paper.
and Competitiveness (grant TM2016-74931). M. B. N. (RYC-2013-13628) and S. H. F. (RYC-2012-12167) acknowledge financial support from the Ramón y Cajal Research Fellowship of the Ministry of Economy and Competitiveness of Spain. We would like to show our gratitude to the anonymous reviewers and the main Editor Prof. Stanley whose comments and suggestions have been very helpful in improving this paper.
Acknowledgements We thank the Editor, Associate Editor and two anonymous referees for their valuable comments and suggestions. This work was presented at the  SIAM Conference on Financial Mathematics and Engineering, Austin , the 9th  World Congress of the Bachelier Finance Society, New York , the 9th European Summer School in Financial Mathematics, Pushkin, and the 5th Berlin Workshop on Mathematical Finance for Young Researchers, Berlin. The authors thank the participants for fruitful comments.