11 research outputs found
Exploratory graph analysis for configural invariance assessment of a test
No full textIn cross-country comparative analyses, self-report survey tools are widely used to examine variations among respondents from different groups,
such as citizens of various nations. An important methodological issue, in this situation, relates to the configural invariance of the measurement
tool, which holds if the latent structure exhibits the same pattern across various groups. To address this issue, we take an exploratory approach
grounded in the paradigm of graph theory. We discuss the use of exploratory graph analysis to assess the configural invariance in the context
of a multi-group comparative analysis with measurement instruments comprised of ordered categorical indicators. In this framework, networks
are utilised to represent latent constructs, and the covariance between observable indicators is explained through a pattern of causal interactions
between the items. Therefore, we postulate that group-specific correlation-based networks would have a comparable structure if the measuring
instrument operates consistently across groups. Network embedding will be utilised to look into the similarity of the network structures estimated
using a Bayesian approach with sparse-inducing priors and mixture models to identify subgroups of homogeneous graphs. We show through a
simulation analysis and real-world applications that the suggested technique can distinguish differences in the latent structure
On the local power of some tests of strict exogeneity in linear fixed effects models
No full textThe local asymptotic power of a variable addition test for strict exogeneity in a linear panel model is derived under near-epoch dependence and a maintained assumption of contem-poraneously exogenous regressors. Local power is found to depend nontrivially on the rel-ative panel size, on the width of the local neighborhood, and on the maintained notion of exogeneity under the alternative. Some (dis)similarities between this test and already existing test principles are explored.(c) 2021 The Author(s). Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/
Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes [Elektronisk resurs]
No full textA multivariate generalisation of the Whittle likelihood is used to extend spectral subsampling MCMC to stationary multivariate time series by subsampling matrix-valued periodogram observations in the frequency domain. To assess the performance of the methodology in challenging problems, a multivariate generalisation of the autoregressive tempered fractionally integrated moving average model (ARTFIMA) is introduced and some of its properties derived. Bayesian inference based on the Whittle likelihood is demonstrated to be a fast and accurate alternative to the exact time domain likelihood. Spectral sub- sampling is shown to provide up to two orders of magnitude additional speed-up, while retaining MCMC sampling efficiency and accuracy, compared to spectral methods using the full dataset. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics. This is an open access article under the CC BY license.</p
Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality
No full textWe study the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We introduce a decomposition of the HDFTS into two distinct components: a deterministic component and a residual component that varies over time. The decomposition is derived through the estimation of two-way functional analysis of variance. A functional time series forecasting method, based on functional principal component analysis, is implemented to produce forecasts for the residual component. By combining the forecasts of the residual component with the deterministic component, we obtain forecast curves for multiple populations. We apply the model to age- and sex-specific mortality rates in the United States, France, and Japan, in which there are 51 states, 95 departments, and 47 prefectures, respectively. The proposed method is capable of delivering more accurate point and interval forecasts in forecasting multi-population mortality than several benchmark methods considered.The first author acknowledges the financial support of the King Abdullah University of Science and Technology (KAUST). The last author acknowledges the funding of an Australian Research Council Discovery Project DP230102250 titled “Feature learning for high-dimensional functional time series” and Macquarie University DataX consilience center. The authors are grateful for the comments from the participants at the Australian National University, University of Auckland, Australian Government Actuary, the 6th International Conference on Econometrics and Statistics (EcoSta 2023), Joint Statistical Meeting, and Australian Statistical Conference in 2023
Identification of independent structural shocks in the presence of multiple Gaussian components
No full textSeveral recently developed identification techniques for structural VAR models are based on the assumption of non-Gaussianity. So-called independence based identification provides unique structural shocks (up to scaling and ordering) under the assumption of at most one Gaussian component. While non-Gaussianity of certain interesting shocks appears rather natural, not all macroeconomic shocks in the system might show this clear difference from Gaussianity. Identifiability can be generalized by noting that even in the presence of multiple Gaussian shocks the non-Gaussian ones are still unique. Consequently, independence based identification allows to uniquely determine the (non-Gaussian) shocks of interest irrespective of the distribution of the remaining system. Furthermore, studying settings close to normality or with multiple Gaussian components highlights the performance of normality diagnostics and their applicability to decide on the identifiability of the structural shock components. In an illustrative five dimensional model the identified monetary policy and stock price shock confirm the results of previous studies on the monetary policy asset price nexus. (C) 2018 The Author. Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics.Peer reviewe
Calcium flux across plant mitochondrial membranes: possible molecular players
No full textPlants, being sessile organisms, have evolved the ability to integrate external stimuli into metabolic and developmental signals. A wide variety of signals, including abiotic, biotic, and developmental stimuli, were observed to evoke specific spatio-temporal Ca2+ transients which are further transduced by Ca2+ sensor proteins into a transcriptional and metabolic response. Most of the research on Ca2+ signaling in plants has been focused on the transport mechanisms for Ca2+ across the plasma- and the vacuolar membranes as well as on the components involved in decoding of cytoplasmic Ca2+ signals, but how intracellular organelles such as mitochondria are involved in the process of Ca2+ signaling is just emerging. The combination of the molecular players and the elicitors of Ca2+ signaling in mitochondria together with newly generated detection systems for measuring organellar Ca2+ concentrations in plants has started to provide fruitful grounds for further discoveries. In the present review we give an updated overview of the currently identified/hypothesized pathways, such as voltage-dependent anion channels, homologs of the mammalian mitochondrial uniporter, LETM1, a plant glutamate receptor family member, adenine nucleotide/phosphate carriers and the permeability transition pore, that may contribute to the transport of Ca2+ across the outer and inner mitochondrial membranes in plants. We briefly discuss the relevance of the mitochondrial Ca2+ homeostasis for ensuring optimal bioenergetic performance of this organelle
Plant cytoplasmic GAPDH: redox post-translational modifications and moonlighting properties
No full textGlyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a ubiquitous enzyme involved in glycolysis and shown, particularly in animal cells, to play additional roles in several unrelated non-metabolic processes such as control of gene expression and apoptosis. This functional versatility is regulated, in part at least, by redox post-translational modifications that alter GAPDH catalytic activity and influence the subcellular localization of the enzyme. In spite of the well established moonlighting (multifunctional) properties of animal GAPDH, little is known about non-metabolic roles of GAPDH in plants. Plant cells contain several GAPDH isoforms with different catalytic and regulatory properties, located both in the cytoplasm and in plastids, and participating in glycolysis and the Calvin-Benson cycle. A general feature of all GAPDH proteins is the presence of an acidic catalytic cysteine in the active site that is overly sensitive to oxidative modifications, including glutathionylation and S-nitrosylation. In Arabidopsis, oxidatively-modified cytoplasmic GAPDH has been successfully used as a tool to investigate the role of reduced glutathione, thioredoxins and glutaredoxins in the control of different types of redox post-translational modifications. Oxidative modifications inhibit GAPDH activity, but might enable additional functions in plant cells. Mounting evidence support the concept that plant cytoplasmic GAPDH may fulfill alternative, non-metabolic functions that are triggered by redox post-translational modifications of the protein under stress conditions. The aim of this review is to detail the molecular mechanisms underlying the redox regulation of plant cytoplasmic GAPDH in the light of its crystal structure, and to provide a brief inventory of the well known redox-dependent multi-facetted properties of animal GAPDH, together with the emerging roles of oxidatively-modified GAPDH in stress signaling pathways in plants
Limits in the use of cPTIO as Nitric Oxide scavenger and EPR probe in plant cells and seedlings
No full textOver the last decade the importance of nitric oxide (NO) in plant signaling has emerged. Despite its recognized biological role, the sensitivity and effectiveness of the methods used for measuring NO concentration in plants are still under discussion. Among these, electron paramagnetic resonance (EPR) is a well-accepted technique to detect NO. In the present work we report the constraints of using 2-4-carboxyphenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO) in biological samples as spin trap for quantitative measurement of NO. EPR analyses on Arabidopsis cell cultures and seedlings show that cPTIO(NNO) is degraded in a matter of few minutes while the (INO) compound, produced by cPTIO and NO reaction, has not been detected. Limitations of using this spin trap in plant systems for quantitative measurements of NO are discussed.As NO scavenger, cPTIO is widely used in combination with 4-amino-5-methylamino-2',7'-difluorofluorescein (DAF) fluorescent dye in plant research. However, the dependence of DAF fluorescence on cPTIO and NO concentrations is not clearly defined so that the range of concentrations should be tightly selected. In this context, a systematic study on cPTIO NO-scavenging properties has been performed, as it was still lacking for plant system applications.The results of this systematic analysis are discussed in terms of reliability of the use of cPTIO in the quantitative determination and scavenging of NO in plants and plant cultured cells
Higher-order statistics for DSGE models
No full textClosed-form expressions for unconditional moments, cumulants and polyspectra of order
higher than two are derived for non-Gaussian or nonlinear (pruned) solutions to DSGE
models. Apart from the existence of moments and white noise property no distributional
assumptions are needed. The accuracy and utility of the formulas for computing
skewness and kurtosis are demonstrated by three prominent models: Smets and Wouters
(AER, 586-606, 97, 2007) (first-order approximation), An and Schorfheide (Econom.
Rev., 113-172, 26, 2007) (second-order approximation) and the neoclassical growth model
(third-order approximation). Both the Gaussian as well as Student's t-distribution are
considered as the underlying stochastic processes. Lastly, the efficiency gain of including
higher-order statistics is demonstrated by the estimation of a RBC model within a
Generalized Method of Moments framework
Functional Imaging in living Plants - Cell Biology meets Physiology
No full textThe study of plant cell physiology is currently experiencing a profound transformation. Novel techniques allow dynamic in vivo imaging with subcellular resolution, covering a rapidly growing range of plant cell physiology. Several basic biological questions that have been inaccessible by the traditional combination of biochemical, physiological and cell biological approaches now see major progress. Instead of grinding up tissues, destroying their organisation, or describing cell- and tissue structure, without a measure for its function, novel imaging approaches can provide the critical link between localisation, function and dynamics. Thanks to a fast growing collection of available fluorescent protein variants and sensors, along with innovative new microscopy technologies and quantitative analysis tools, a wide range of plant biology can now be studied in vivo, including cell morphology & migration, protein localization, topology & movement, protein-protein interaction, organelle dynamics, as well as ion, ROS & redox dynamics. Within the cell, genetic targeting of fluorescent protein probes to different organelles and subcellular locations has started to reveal the stringently compartmentalized nature of cell physiology and its sophisticated spatiotemporal regulation in response to environmental stimuli. Most importantly, such cellular processes can be monitored in their natural 3D context, even in complex tissues and organs – a condition not easily met in studies on mammalian cells. Recent new insights into plant cell physiology by functional imaging have been largely driven by technological developments, such as the design of novel sensors, innovative microscopy & imaging techniques and the quantitative analysis of complex image data. Rapid further advances are expected which will require close interdisciplinary interaction of plant biologists with chemists, physicists, mathematicians and computer scientists. High-throughput approaches will become increasingly important, to fill genomic data with ‘life’ on the scale of cell physiology. If the vast body of information generated in the -omics era is to generate actual mechanistic understanding of how the live plant cell works, functional imaging has enormous potential to adopt the role of a versatile standard tool across plant biology and crop breeding. We welcome original research papers, methodological papers, reviews and mini reviews, with particular attention to contributions in which novel imaging techniques enhance our understanding of plant cell physiology and permits to answer questions that cannot be easily addressed with other techniques
