PLS-SEM algorithm for the decision to purchase durian milk with seeds

This study explores and examines the relationship between the success of Research and Development (R&D) consortia and the factors that determine this success. Most studies of R&D consortium success are based on a set of observable variables as antecedents of this success. In our study, we recognise the complexity of the problem and use latent variables as a set of weighted observable variables, rather than classical variables. Empirical insights are provided by applying a second-generation technique, namely the PLS-SEM algorithm, to analyse the data gathered from R&D consortia in Spain. The results demonstrate the existence of constructs (partner attributes, alliance attributes, environment attributes and leadership) that encompass the determinant factors for success, and also show that the attributes of the partners and the characteristics of the project alliances exert a positive influence on the success of the joint R&D project.

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Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm

Journal of the American Statistical Association ◽

10.1080/01621459.1991.10475130 ◽

1991 ◽

Vol 86 (416) ◽

pp. 899-909 ◽

Cited By ~ 229

Author(s):

Xiao-Li Meng ◽

Donald B. Rubin

Keyword(s):

Asymptotic Variance ◽

Covariance Matrices ◽

Sem Algorithm

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Probabilistic Mixtures in Measurements of Interterritorial Differentiation

Voprosy statistiki ◽

10.34023/2313-6383-2020-27-3-53-64 ◽

2020 ◽

Vol 27 (3) ◽

pp. 53-64

Author(s):

V. V. Glinskiy ◽

Yu. N. Ismaiylova

Keyword(s):

Em Algorithm ◽

Russian Federation ◽

Probability Distributions ◽

Economic Inequality ◽

Small Sample ◽

Federal State ◽

Time Interval ◽

Statistical Population ◽

Sem Algorithm ◽

The Russian Federation

The article summarizes research results of the study on the problem of assessing the differentiation level of socio-economic development of territorial units of the Russian Federation. The authors propose an approach to measuring differentiation using mixtures of probability distributions.This technique was developed and tested on real data that allows one to determine the presence or absence of interregional differentiation. The research hypothesis, that interterritorial differentiation is estimated by a speciﬁc statistical indicator selected based on a content, qualitative analysis, served as a theoretical platform of this methodology. Differentiation is practically absent if the entire statistical population is described by a single law of probability distribution. If the statistical population is described by a mixture of probability distributions, then one should expect the presence of a signiﬁcant level of differentiation by the considered indicator.In mathematical statistics, the problem of separating a mixture of probability distributions (estimating parameters of distribution densities and weighting coefficients) is traditionally solved using several similar methods. For example, the expectation-maximization (EM) algorithm, median modiﬁcations of the EM-algorithm, SEM-algorithm, taking into account the speciﬁcs of the selected object (constituent entities of the Russian Federation a small sample). To solve this problem, the authors used the SEM algorithm. As the information base of the empirical study, official statistics were used (open data from the Federal State Statistics Service).The typologies of the constituent entities of the Russian Federation were identiﬁed based on two characteristics within 2005-2017-time interval. The ﬁrst one being the level of violence (using the “homicide rate” indicator the number of homicides and attempted murders per 100000 population). And second average per capita income, which made it possible, among other things, to additionally test the hypothesis of the traditional use of differentiation trends in the level of violence as an indicator of economic inequality. According to the authors, the results of this study can be used as instrumental and informational support for managerial decisions aimed at regulating the differentiation of Russian regions by the level of violence and economic inequality.

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Kernel density smoothing of composite spatial data on administrative area level

AStA Wirtschafts- und Sozialstatistisches Archiv ◽

10.1007/s11943-021-00298-9 ◽

2021 ◽

Author(s):

Kerstin Erfurth ◽

Marcus Groß ◽

Ulrich Rendtel ◽

Timo Schmid

Keyword(s):

Uniform Distribution ◽

Ethnic Minorities ◽

Spatial Data ◽

Kernel Density ◽

Implicit Assumption ◽

Density Estimates ◽

Sem Algorithm ◽

Kernel Density Estimates ◽

Choropleth Maps ◽

Administrative Area

AbstractComposite spatial data on administrative area level are often presented by maps. The aim is to detect regional differences in the concentration of subpopulations, like elderly persons, ethnic minorities, low-educated persons, voters of a political party or persons with a certain disease. Thematic collections of such maps are presented in different atlases. The standard presentation is by Choropleth maps where each administrative unit is represented by a single value. These maps can be criticized under three aspects: the implicit assumption of a uniform distribution within the area, the instability of the resulting map with respect to a change of the reference area and the discontinuities of the maps at the borderlines of the reference areas which inhibit the detection of regional clusters.In order to address these problems we use a density approach in the construction of maps. This approach does not enforce a local uniform distribution. It does not depend on a specific choice of area reference system and there are no discontinuities in the displayed maps. A standard estimation procedure of densities are Kernel density estimates. However, these estimates need the geo-coordinates of the single units which are not at disposal as we have only access to the aggregates of some area system. To overcome this hurdle, we use a statistical simulation concept. This can be interpreted as a Simulated Expectation Maximisation (SEM) algorithm of Celeux et al (1996). We simulate observations from the current density estimates which are consistent with the aggregation information (S-step). Then we apply the Kernel density estimator to the simulated sample which gives the next density estimate (E-Step).This concept has been first applied for grid data with rectangular areas, see Groß et al (2017), for the display of ethnic minorities. In a second application we demonstrated the use of this approach for the so-called “change of support” (Bradley et al 2016) problem. Here Groß et al (2020) used the SEM algorithm to recalculate case numbers between non-hierarchical administrative area systems. Recently Rendtel et al (2021) applied the SEM algorithm to display spatial-temporal clusters of Corona infections in Germany.Here we present three modifications of the basic SEM algorithm: 1) We introduce a boundary correction which removes the underestimation of kernel density estimates at the borders of the population area. 2) We recognize unsettled areas, like lakes, parks and industrial areas, in the computation of the kernel density. 3) We adapt the SEM algorithm for the computation of local percentages which are important especially in voting analysis.We evaluate our approach against several standard maps by means of the local voting register with known addresses. In the empirical part we apply our approach for the display of voting results for the 2016 election of the Berlin parliament. We contrast our results against Choropleth maps and show new possibilities for reporting spatial voting results.

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