Terms Of Statistics Research Articles

Eksplozja przedsiębiorczości w Polsce po 1988 roku. Przyczyny i skutki (do 1 maja 2004)

This article explains the impact of the explosion of Polish entrepreneurship (the so-called founding privatization) on the modernization of social structure and free market institutions in Poland. It has been shown that the process of revival of entrepreneurship after 1988 took place with the use of experiences included in the concept of heritage of the past. Changes in the ownership structure of economic organizations observed after 1988 are discussed in statistical terms. This change was of great importance for the modernization of the company size structure in Poland. The fast-paced process of rebuilding the social group of entrepreneurs played a major role in building family businesses. They are the stabilizer of the economy. For the efficiency of the modernization process in Poland after 1988, it was important to achieve institutional balance. The practice of everyday life showed that it was a difficult task to achieve a satisfactory state. As a result, the slowing down of the process of qualitative change in Poland after 1988 was largely influenced by informal institutions, including tradition and customs established in the past.

Medium- and Long-Term Prediction of Polar Motion Using Weighted Least Squares Extrapolation and Vector Autoregressive Modeling

ABSTRACT This article presents the application of weighted least squares (WLS) extrapolation and vector autoregressive (VAR) modeling in polar motion prediction. A piecewise weighting function is developed for the least squares (LS) adjustment in consideration of the effect of intervals between observation and prediction epochs on WLS extrapolation. Furthermore, the VAR technique is used to simultaneously model and predict the residuals of x p, y p pole coordinates for WLS misfit. The simultaneous predictions of x p, y p pole coordinates are subsequently computed by the combination of WLS extrapolation of harmonic models for the linear trend, Chandler and annual wobbles, and VAR stochastic prediction of the residuals (WLS+VAR). The 365-day-ahead x p, y p predictions are compared with those generated by LS extrapolation+univariate AR prediction and LS extrapolation+VAR modeling. It is shown that the x p, y p predictions based on WLS+VAR taking into consideration both the interval effect and correlation between x p and y p outperform those generated by two others. The accuracies of the x p predictions are 13.97 mas, 18.47 mas, and 20.52 mas, respectively for the 150-, 270-, and 365-day horizon in terms of the mean absolute error statistics, 36%, 24.8%, and 33.5% higher than LS+AR, respectively. For the y p predictions, the 150-, 270-, and 365-day accuracies are 15.41 mas, 21.17 mas, and 21.82 mas respectively, 27.4%, 11.9%, and 21.8% higher than LS+AR respectively. Moreover, the absolute differences of the WLS+VAR predictions and observations are smaller than the differences from LS+VAR and LS+AR, which is practically important to practical and scientific users, although the improvement in accuracies is no more than 10% relative to LS+VAR. The further comparison with the predictions submitted to the 1st Earth Orientation Parameters Prediction Comparison Campaign (1st EOP PCC) shows that while the accuracy of the predictions within 30 days is comparable with that by the most accurate prediction techniques including neural networks and LS+AR participating in the campaign for x p, y p pole coordinates, the accuracy of the predictions up to 365 days into the future are better than accuracies by the other techniques except best LS+AR used in the EOP PCC. It is therefore concluded that the medium- and long-term prediction accuracy of polar motion can be improved by modeling x p, y p pole coordinates together.

Consistency of the Structural Properties of the BFI-10 Across 16 Samples From Eight Large-Scale Surveys in Germany

Abstract: The assessment of the Big Five personality domains is standard practice in most large-scale social surveys nowadays. The instrument most widely used for this purpose is the BFI-10, an ultra-short measure assessing each Big Five domain with two items. Recent studies have identified issues with the structural properties of the BFI-10, especially its factorial validity. To investigate whether these issues arise from the instrument itself or biases due to translation or sampling, we examined the extent to which the structural properties of the BFI-10 in terms of descriptive statistics, intercorrelations, reliability, and factorial validity vary when keeping the target population and language constant. Results revealed that, across 16 independent samples (total N ~ 60,000) from eight large-scale surveys representative of the adult population in Germany, the structural properties of the BFI-10 were (a) largely consistent and (b) mostly adequate. Most importantly, in nearly all samples, patterns of loading were congruent with an idealized Big Five structure, thereby supporting factorial validity. These results demonstrate that the structural properties of the BFI-10 are highly stable and replicable in large-scale samples. Especially given its brevity, the BFI-10 can thus be regarded as adequate for use in large-scale survey settings.

Vertical migration externalities

State income taxes affect federal income tax revenue by shifting the spatial distribution of households between high- and low-productivity states, thereby changing household incomes and tax payments. We derive an expression for these fiscal externalities of state taxes in terms of estimable statistics. An empirical quantification using American Community Survey data reveals that the externalities range from large and negative in some states, to large and positive in others. In California, an increase in the state income tax rate and the resulting change in the distribution of households across states lead to a decrease in federal income tax revenue of 39 cents for every dollar of California tax revenue raised. The externality amounts to a 0.27% decrease in total federal income tax revenue for a 1 pp increase in California’s state tax rate. Our results raise the possibility that state taxes may be set too high in high-productivity states, and set too low in low-productivity states.

Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods

ABSTRACT Plant functional traits (e.g. leaf pigment and water contents, specific leaf area) serve as important indicators of plant condition, both within a given vegetation season and between years. Remote sensing-based methods allow for non-destructive and repeatable monitoring of the Earth’s surface and thus offer an efficient way to map and monitor these traits. In our study, we used a large database of ground survey data sampled at several contrasting phenological phases of vegetation to develop and compare different machine learning models trained to estimate selected plant functional traits at two different sites: mixed floodplain forest at Lanžhot and beech forest at Štítná, both in the Czech Republic. Empirical models were trained as predictors using 1) Sentinel-2 satellite data (a data set with higher spatial and spectral resolution), and 2) Harmonized Landsat Sentinel-2 (HLS) product (a data set with higher temporal resolution). The most successfully retrieved traits were chlorophyll and carotenoid content (R2 = 0.78 and 0.65, respectively). Although models trained with Sentinel-2 predictors proved to be slightly better in terms of validation statistics compared to HLS predictors, the HLS product may be preferable for some applications requiring analysis at a high frequency. The best-performing machine learning algorithm, canonical correlation forest, was then applied per pixel to all cloud-free images from the HLS product at both study sites for the years 2019–2021. This allowed us to create a time series of plant functional traits useful for observing differences between the two sites, as well as between growing seasons, and also to observe patterns of spatial behaviour using map outputs.

Federated learning with superquantile aggregation for heterogeneous data

We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail statistics of the error distribution over heterogeneous clients. We present a stochastic training algorithm that interleaves differentially private client filtering with federated averaging steps. We prove finite time convergence guarantees for the algorithm: $$O(1/\sqrt{T})$$ in the nonconvex case in T communication rounds and $$O(\exp (-T/\kappa ^{3/2}) + \kappa /T)$$ in the strongly convex case with local condition number $$\kappa$$ . Experimental results on benchmark datasets for federated learning demonstrate that our approach is competitive with classical ones in terms of average error and outperforms them in terms of tail statistics of the error.

Does Financial Deepening Foster Clean Energy Sustainability over Conventional Ones? Examining the Nexus between Financial Deepening, Urbanization, Institutional Quality, and Energy Consumption in China

Energy availability and the selection of suitable energy sources have substantial implications on both economic and environmental sustainability, and it is because the environmental protection cost is directly linked to overall energy inclusion in the economy. Thus, the importance of clean energy has been noticed in the literature regardless of the economic structure. The purpose of the study is to discover the effects of financial deepening (FD), urbanization (UR), and institutional quality (IQ) on China’s energy consumption. Annual time series date for 1985 to 2019 utilized for documenting the coefficients of explanatory variables by implementing both linear and nonlinear Autoregressive Distributed Lagged (ARDL) and the Fourier-TY causality test. In terms of the test statistics for combined and Maki cointegration, the study revealed that a long-run association prevails in the empirical nexus. Moreover, the symmetric and asymmetric framework established long-run associations. Referring to the coefficients of financial deepening, UR, and governmental effectiveness, the study found a statistically significant and favorable impression of REC. While financial deepening and governmental effectiveness unveiled negatively influenced NREC and fossil energy consumption. The asymmetric linkage between explained and explanatory variables was confirmed through the execution of a standard weld test with a null symmetry. The asymmetry coefficients of FD, UR, and IQ were positive and statistically significant at the 1% level in both the long and short runs. The directional causality revealed feedback hypothesis holds in understanding the causal relationship between explanatory factors and RE usage. The policy recommendations for the future were generated from the research findings.

Carr and Wu’s (2020) framework in the oil ETF option market

This paper studies the information inferred from the Carr and Wu’s (2020) formula based on a new option pricing framework in the United States Oil Fund (USO) options. We first document the term structure and dynamics of the risk-neutral variance and covariance rates which lead to a “U”-shaped implied volatility smile with a positive curvature. We then investigate the return predictability of the innovations in the risk-neutral variance and covariance rates (DRNV and DRNC) and their term structures (TRNV and TRNC) and find that DRNC is a significant and robust predictor to forecast daily, weekly and monthly USO excess returns in both statistical and economic terms based on in-sample and out-of-sample tests.

Surreal space in René Magritte's Le Blanc-Seing (1965).

"The function of painting is to make poetry visible… to render thought visible." René Magritte Pictorial art reveals some of the visual brain's "neural rules" and processing hierarchy. This article examines one salient exemplar drawn from the vast oeuvre of the great Belgian surrealist, René Magritte (1898-1967). The painting Le Blanc-Seing (1965) is a virtual course in perception, with many elements illustrating figure-ground segregation, object identification, cues for depth perception, Gestalt Laws of occlusion-continuation, and visual scene organization. Le Blanc-Seing is visually stunning, beautifully rendered, and, at first glance, otherwise unremarkable. However, Magritte has embedded several jarring surreal effects in the painting that provide clues about the visual brain's visual processing hierarchy in scene construction. This includes elements whose alternation between two incompatible percepts cannot be explained in terms of local spatiochromatic statistics (Ritchie & van Buren, 2020). Finally, I provide a plausible pictorial inspiration (never before demonstrated) for the painting in a brief scene from a 1924 German silent film.

Estimating the VaR-induced Euler allocation rule

AbstractThe prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes – using a statistical term – the quantile-regression (QR) function. Although the cumulative QR function (i.e., an integral of the QR function) has received considerable attention in the literature, a fully developed statistical inference theory for the QR function itself has been elusive. In the present paper, we develop such a theory based on an empirical QR estimator, for which we establish consistency, asymptotic normality, and standard error estimation. This makes the herein developed results readily applicable in practice, thus facilitating decision making within the RORAC paradigm, conditional mean risk sharing, and current regulatory frameworks.

Insights Into Vietnamese Bilingual Students’ Quizlet-Based English Vocabulary Learning

Modern technology has revolutionized the education by offering various tools that can enhance the teaching and learning process in general and English language teaching and learning in specific. Among a wide range of educational apps, Quizlet has been applied in vocabulary teaching and learning in different contexts. Therefore, this study aims at investigating bilingual students’ Quizlet-based English vocabulary learning. It involved a cohort of 50 bilingual students, who were conveniently sampled from an international school in Vietnam, in answering the questionnaires. The collected data were analysed in terms of descriptive statistics with the use of software SPSS. The findings have shown that bilingual students perceived Quizlet as a useful, enjoyable, easy, and convenient tool for English vocabulary language learning, and they reckoned that Quizlet could improve their vocabulary learning. Based on such findings, this study has come up with pedagogical implications for the use of Quizlet in English vocabulary teaching and learning in the research context and other similar ones.

A UNIFORM PLACEMENT OF ALTERS ON SPHERICAL SURFACE (U-PASS) FOR EGO-CENTRIC NETWORKS WITH COMMUNITY STRUCTURE AND ALTER ATTRIBUTES

An ego-centric network consists of a particular node (ego) that has relationships to all neighboring nodes (alters) in the network. Such network serves as an important tool to study the network structure of alters of the ego, and it is essential to present such network with good visualization. This work aims at introducing an efficient method, namely the Uniform Placement of Alters on Spherical Surface (U-PASS), to represent an ego-centric network so that all alters are scattered on the surface of the unit sphere uniformly. Unlike other simple uniformity that considers to maximize Euclidean distances among nodes, U-PASS is a three-stage method that spreads the alters with the consideration of existing edges among alters, no overlapping of node clusters, and node attribute information. Particle swarm optimization is employed to improve efficiency in node allocations. To guarantee the uniformity, we show the connection between our U-PASS to the minimum energy design on a two-dimensional flat plane with a specific gradient. Our simulation study shows good performance of U-PASS in terms of some distance statistics when compared to four state-of-the-art methods via self-organizing maps and force-driven approaches. We use a Facebook network to illustrate how this ego-centric network looks different after the alter nodes are allocated via our U-PASS.

Impact of Human Mobility on COVID-19 Transmission According to Mobility Distance, Location, and Demographic Factors in the Greater Bay Area of China: Population-Based Study.

Mobility restriction was one of the primary measures used to restrain the spread of COVID-19 globally. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost 3 years, which caused severe adverse outcomes in terms of health, society, and economy. This study aimed to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, location, and demographic factors in order to identify hotspots of transmission and guide public health policies. Large volumes of anonymized aggregated mobile phone position data between January 1 and February 24, 2020, were collected for 9 megacities in the Greater Bay Area, China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroup analysis was also performed for sex, age, travel location, and travel distance. Statisticalinteractionterms were includedin a variety of models that express different relations between involved variables. The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 GR among people aged 50-59 years (GR decrease of 13.17% per 10% reduction in mobility volume; P<.001) than among other age groups (GR decreases of 7.80%, 10.43%, 7.48%, 8.01%, and 10.43% for those aged ≤18, 19-29, 30-39, 40-49, and ≥60 years, respectively; P=.02 for the interaction). The impact of mobility reduction on COVID-19 transmission was higher for transit stations and shopping areas (instantaneous reproduction number [Rt] decreases of 0.67 and 0.53 per 10% reduction in mobility volume, respectively) than for workplaces, schools, recreation areas, and other locations (Rt decreases of 0.30, 0.37, 0.44, and 0.32, respectively; P=.02 for the interaction). The association between mobility volume reduction and COVID-19 transmission was lower with decreasing mobility distance as there was a significant interaction between mobility volume and mobility distance with regard to Rt (P<.001 for the interaction). Specifically, the percentage decreases in Rt per 10% reduction in mobility volume were 11.97% when mobility distance increased by 10% (Spring Festival), 6.74% when mobility distance remained unchanged, and 1.52% when mobility distance declined by 10%. The association between mobility reduction and COVID-19 transmission significantly varied according to mobility distance, location, and age. The substantially higher impact of mobility volume on COVID-19 transmission for longer travel distance, certain age groups, and specific travel locations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that can monitor movement at a detailed level to measure the potential impacts of future pandemics.

Developing Weibull-based diameter distributions for the major coniferous species in Heilongjiang Province, China

Diameter distribution models play an important role in forest inventories, growth prediction, and management. The Weibull probability density function is widely used in forestry. Although a number of methods have been proposed to predict or recover the Weibull distribution, their applicability and predictive performance for the major tree species of China remain to be determined. Trees in sample plots of three even-aged coniferous species (Larix olgensis, Pinus sylvestris and Pinus koraiensis) were measured both in un-thinned and thinned stands to develop parameter prediction models for the Weibull probability density function. Ordinary least squares (OLS) and maximum likelihood regression (MLER), as well as cumulative distribution function regression (CDFR) were used, and their performance compared. The results show that MLER and CDFR were better than OLS in predicting diameter distributions of tree plantations. CDFR produced the best results in terms of fitting statistics. Based on the error statistics calculated for different age groups, CDFR was considered the most suitable method for developing prediction models for Weibull parameters in coniferous plantations.

Affecting Factors on The Timing of The Issuance of The Report of The External Auditor of The Accounts of Iraqi Banks

This study aims to study the effect of many factors (Audit office size, disclosure instructions according to the Iraqi Securities Commission, the size of audit fees) on the timing of the issuance of the report of the external auditor of the accounts of Iraqi banks. The sample consists of (19) Iraqi banks listed in the Iraqi market for securities for the period from 2012 to 2020,&nbsp;&nbsp; To achieve the objectives of the research, a model was developed using multiple regression, which included the factors above, such as independent variables and&nbsp; the timing of issuing external auditor reports as a dependent variable. The results showed that the regression model is highly significant in statistical terms and therefore reliable to, where the value of F was (80.412) with a significant level of 0.000, and therefore it can be relied upon to predict the values of the dependent variable.&nbsp; The results showed that Iraqi banks issue their external auditor reports at an average of (130.877) days from the end of the financial year.&nbsp; The results of the study showed the existence of a statistically significant significant relationship between the number of auditing offices and the time taken for the examination and audit process and the issuance of the external auditor's report for the accounts of Iraqi banks listed in the Iraq Stock Exchange. The results indicate the existence of a positive, moral relationship with statistical significance between the disclosure instructions of the Securities Commission and the time taken for the examination and auditing process and the issuance of the published external auditor's report for the Iraqi shareholding banks. This indicates that the external auditor adheres to the disclosure instructions issued by the Iraqi Securities Commission.&nbsp; The results indicate that there is no significant statistically significant relationship between the size of the auditing fees and the time taken for the examination and auditing process and the issuance of the external auditor's report for the accounts of Iraqi banks listed in the Iraq Stock Exchange.

Analysis on roles of thermal radiation to evaporation and combustion of fuel droplets

Thermal radiation is a significant mode of energy transmission besides conduction and convection in most combustion systems. Accurate treatment of thermal radiation is a crucial element for predicting the flame structure and pollutant formation. However, thermal radiation to fuel droplet evaporation is often neglected in the numerical simulations of two-phase spray combustion systems. In this paper, computational analysis on the thermal radiation to evaporation and combustion of fuel droplets in two-phase spray combustion is conducted. The influence of flame radiation on droplet evaporation and combustion is studied by adding radiation source term to the energy equation describing droplet evaporation process. Radiation transfer equation is solved, in which the radiation of CO2, H2O and soot is imposed to consider radiation effect of the flame. The partially premixed combustion model is employed, in which radiation heat transfer between droplet and ambient gases is taken into account. A reduced chemical reaction mechanism for n-heptane consisting of 76 species and 349 reactions is used. Four different numerical setups have been used and compared, and the simulation results coincide with the experiment data in terms of spray statistics and flame shapes. The peak flame temperature increases by 28 K when the radiative heat source term in the evaporation process is considered. The droplet evaporation is significantly enhanced by thermal radiation, leading to a promotion of 34.8% in C7H16 concentration in the center region of combustor. The concentration of combustion products, such as H2O and CO2, increases by 13.2% in the reaction region.

THE INFLUENCE OF URBAN ENVIRONMENT FACTORS ON THE GROWTH OF HORSE CHESTNUT AESCULUS HIPPOCASTANUM L.

Aim of the study: In the years 1995–1999, at 21 sites within the broadly understood center of Poznań, research on the development of chestnut Aesculus hippocastanum L. was carried out. As part of this research, selected environmental factors were analysed. The aim was to become familiar with the conditions of tree development in the urban and industrial agglomeration of Poznań. Material and methods: Knowledge of tree development limiting factors in urban conditions allows to take actions that will eliminate them or limit their negative impact on the development of trees, including their biometric parameters. The implementation of such actions will neutralise or compensate for undesirable processes in the urban environment. As a consequence, it will allow for unlimited environment-generating functions by trees such as: CO2 assimilation, oxygen release, water vapour transpiration, dust retention, phytoncide production or noise suppression. Results and conclusions: Research on the development of 35 chestnut trees at 21 streets included: seasonal periodicity of trees, recording air temperature and humidity, physico-chemical analysis of the substrate, biometric measurements and, at eight sites, selected physiological parameters, such as photosynthesis and transpiration. In total, over 17,000 numerical data about the environment were collected. The graphs in this paper present mainly the relationship between environmental parameters and biometric measurements at a highly significant level in terms of statistics, i.e. for p ≤ 0.01.

STATISTICS APPLIED WITH R USING ARTIFICIAL INTELLIGENCE

Machine learning within R using inferential statistics in academic fields today is already a reality, that research is directed towards the world of artificial intelligence as an aid within engineering based on tools such as machine learning, in this case with supervised learningand unsupervised learning. It is important because it can be applied in engineering, such as in statistics and the world of artificial intelligence to automate statistical processesof various samples in this area.&#x0D; Objective. Perform adescriptive statistic using quantitative data in R through structured programming compared with supervised and unsupervised learning algorithms for decision making in engineering.&#x0D; Methodology. This research is descriptive, uses the Methodology of Construction of an Algorithm for the systemic Learning of students of the first semester of the subject of ICTs (Mejía E. et al, 2018) adapted to the use of artificial intelligence algorithms, which provides effective methods that allow to implement adescriptive statistic, to perform programs using functions and procedures within R. Tests were carried out with third-semester students of the ESPOCH Statistics career to determine with structured programming (before) and artificial intelligence algorithms (after).&#x0D; Results. Students using structured programming to obtain these statistics only 36.84% reach n to complete the work, while students using artificial intelligence algorithms reach 84.21%, to conclude the work.&#x0D; Conclusion. It is concluded that under the parameters of use of artificial intelligence algorithms to obtain a descriptive statistic for engineering, makes decisions with percentages that favor these techniques provided by the researchersis, this result He tells us that the second option is the best, obtaining in statistical terms very favorableconditions to insert this technique and methodology in engineering environments. Students with these algorithms that use supervised and unsupervised learning will have an extra plus when performing this type of statistics in the professional environment.

International visibility of Armenian domestic journals: the role of scientific diaspora

Abstract Purpose Nearly 122 scientific journals are currently being published in Armenia—of which only six are indexed by WoS and/or Scopus databases. The majority of the national journals are published in the Armenian language, solely possessing abstracts written in English, although there are also English-language and multi-language journals with articles not only in Armenian but also in other foreign languages. The aim of this article is to study the visibility of the (non-indexed) national Armenian journals in the WoS database through citation analysis. In consideration of the existence of a relevant Armenian “diaspora” in the world, this article also attempts to estimate its impact in terms of citation statistics. Design/methodology/approach For this end, we have identified citations to the national/domestic Armenian journals in the WoS database in comparison with the share of citations received from “diaspora” researchers (researchers of Armenian origin born in foreign countries and those originally from Armenia who have emigrated to foreign countries). Findings Among the 116 Armenian domestic journals analyzed (not indexed by WoS), only 47 were found to be cited in WoS. Of these journals, almost 12% are citations by “diaspora” researchers, most of which concern Social Science and Humanities journals. Research limitations Although the surnames of Armenians end with -i(y)an, sometimes, the Diaspora Armenians, surnames are changed or modified or they are not ending with -i(y)an, in this case we may fail to identify them. Practical implications This study can help to build new, more deep and comprehensive relations with scientific diasporas. Originality/value This study offers a new understanding of multifaced research collaboration with scientific diasporas and their role in internationalization of domestic journals.

An Introduction to Statistics for Librarians (Part Two):

In Part One of this column, the different types of data were discussed. Understanding the type of data is essential to interpreting them. If the type of data isn’t correctly identified, it’s not possible to answer some fundamental questions accurately. One of these fundamental questions is “what’s the average value?” This is often the building block for more advanced statistical tests. In statistical terms, this question is asking us for the central tendency of the data. The central tendency is a single value that represents the midpoint of the data set. It tells us what is “average” or “normal” in the data set. There are three different ways to measure central tendency: mode, median, and mean. The measure chosen will depend on the type of data and the distribution of that data.