A Direct Estimation of High Dimensional Stationary Vector Autoregressions

The vector autoregressive (VAR) model is a powerful tool in modeling complex time series and has been exploited in many fields. However, fitting high dimensional VAR model poses some unique challenges: On one hand, the dimensionality, caused by modeling a large number of time series and higher order autoregressive processes, is usually much higher than the time series length; On the other hand, the temporal dependence structure in the VAR model gives rise to extra theoretical challenges. In high dimensions, one popular approach is to assume the transition matrix is sparse and fit the VAR model using the "least squares" method with a lasso-type penalty. In this manuscript, we propose an alternative way in estimating the VAR model. The main idea is, via exploiting the temporal dependence structure, to formulate the estimating problem into a linear program. There is instant advantage for the proposed approach over the lasso-type estimators: The estimation equation can be decomposed into multiple sub-equations and accordingly can be efficiently solved in a parallel fashion. In addition, our method brings new theoretical insights into the VAR model analysis. So far the theoretical results developed in high dimensions (e.g., Song and Bickel (2011) and Kock and Callot (2012)) mainly pose assumptions on the design matrix of the formulated regression problems. Such conditions are indirect about the transition matrices and not transparent. In contrast, our results show that the operator norm of the transition matrices plays an important role in estimation accuracy. We provide explicit rates of convergence for both estimation and prediction. In addition, we provide thorough experiments on both synthetic and real-world equity data to show that there are empirical advantages of our method over the lasso-type estimators in both parameter estimation and forecasting.Comment: 36 pages, 3 figur

A direct estimation of high dimensional stationary vector autoregressions. arXiv preprint arXiv:1307.0293

Abstract The vector autoregressive (VAR) model is a powerful tool in learning complex time series and has been exploited in many fields. The VAR model poses some unique challenges to researchers: On one hand, the dimensionality, introduced by incorporating multiple numbers of time series and adding the order of the vector autoregression, is usually much higher than the time series length; On the other hand, the temporal dependence structure naturally present in the VAR model gives rise to extra difficulties in data analysis. The regular way in cracking the VAR model is via "least squares" and usually involves adding different penalty terms (e.g., ridge or lasso penalty) in handling high dimensionality. In this manuscript, we propose an alternative way in estimating the VAR model. The main idea is, via exploiting the temporal dependence structure, formulating the estimating problem to a linear program. There is instant advantage of the proposed approach over the lassotype estimators: The estimation equation can be decomposed to multiple sub-equations and accordingly can be solved efficiently using parallel computing. Besides that, we also bring new theoretical insights into the VAR model analysis. So far the theoretical results developed in high dimensions (e.g.

Assessing the decarbonization potential of China’s light-duty truck fleet by electrification

Light-duty trucks (LDTs) play an important role in last-mile delivery. To assess the CO2 mitigation potential of the electrification pathway of the LDT fleet in China, a dynamic fleet-based life cycle assessment model is developed. Four scenarios with different electrification progress are designed to evaluate the LDT stock growth, energy demand and the life cycle CO2 emissions quantitatively. The results show that LDT stock will increase to about 38 million in 2050. Energy demand and life cycle CO2 emissions of the LDT fleet can be greatly reduced with the increase in the proportion of battery electric vehicles (BEVs) in the LDT stock. The electrification mitigation benefit obtained from the fuel cycle will eventually outweigh the additional CO2 emissions generated by the vehicle cycle, especially in the long term with a cleaner power grid and a higher proportion of BEV in the LDT stock

Long non-coding RNA CASC2 suppresses pulmonary artery smooth muscle cell proliferation and phenotypic switch in hypoxia-induced pulmonary hypertension

Abstract Background In this study, we aimed to investigate whether and how lncRNA CASC2 was involved in hypoxia-induced pulmonary hypertension (PH)-related vascular remodeling. Methods The expression of lncRNAs or mRNAs was detected by qRT-PCR, and western blot analysis or immunochemistry was employed for detecting the protein expression. Cell number assay and EdU (5-ethynyl-2′-deoxyuridine) staining were performed to assess cell proliferation. Besides, flow cytometry and wound healing assay were employed for assessments of cell apoptosis and cell migration, respectively. Rat model of hypoxic PH was established and the hemodynamic measurements were performed. Hematoxylin and eosin (HE) and Masson′s trichrome staining were carried out for pulmonary artery morphometric analysis. Results The expression of lncRNA CASC2 was decreased in hypoxia-induced rat pulmonary arterial tissues and pulmonary artery smooth muscle cells (PASMCs). Up-regulation of lncRNA CASC2 inhibited cell proliferation, migration yet enhanced apoptosis in vitro and in vivo in hypoxia-induced PH. Western blot analysis and immunochemistry showed that up-regulation of lncRNA CASC2 greatly decreased the expression of phenotype switch-related marker α-SMA in hypoxia-induced PH. Furthermore, it was indicated by the pulmonary artery morphometric analysis that lncRNA CASC2 suppressed vascular remodeling of hypoxia-induced rat pulmonary arterial tissues. Conclusion LncRNA CASC2 inhibited cell proliferation, migration and phenotypic switch of PASMCs to inhibit the vascular remodeling in hypoxia-induced PH

Integrative Transcriptomic and Proteomic Analysis Reveals an Alternative Molecular Network of Glutamine Synthetase 2 Corresponding to Nitrogen Deficiency in Rice (Oryza sativa L.)

Nitrogen (N) is an essential nutrient for plant growth and development. The root system architecture is a highly regulated morphological system, which is sensitive to the availability of nutrients, such as N. Phenotypic characterization of roots from LY9348 (a rice variety with high nitrogen use efficiency (NUE)) treated with 0.725 mM NH4NO3 (1/4N) was remarkable, especially primary root (PR) elongation, which was the highest. A comprehensive analysis was performed for transcriptome and proteome profiling of LY9348 roots between 1/4N and 2.9 mM NH4NO3 (1N) treatments. The results indicated 3908 differential expression genes (DEGs; 2569 upregulated and 1339 downregulated) and 411 differential abundance proteins (DAPs; 192 upregulated and 219 downregulated). Among all DAPs in the proteome, glutamine synthetase (GS2), a chloroplastic ammonium assimilation protein, was the most upregulated protein identified. The unexpected concentration of GS2 from the shoot to the root in the 1/4N treatment indicated that the presence of an alternative pathway of N assimilation regulated by GS2 in LY9348 corresponded to the low N signal, which was supported by GS enzyme activity and glutamine/glutamate (Gln/Glu) contents analysis. In addition, N transporters (NRT2.1, NRT2.2, NRT2.3, NRT2.4, NAR2.1, AMT1.3, AMT1.2, and putative AMT3.3) and N assimilators (NR2, GS1;1, GS1;2, GS1;3, NADH-GOGAT2, and AS2) were significantly induced during the long-term N-deficiency response at the transcription level (14 days). Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that phenylpropanoid biosynthesis and glutathione metabolism were significantly modulated by N deficiency. Notably, many transcription factors and plant hormones were found to participate in root morphological adaptation. In conclusion, our study provides valuable information to further understand the response of rice roots to N-deficiency stress

RNA sequencing provides insights into the evolution of lettuce and the regulation of flavonoid biosynthesis

Horticultural lettuce varieties vary considerably in phenotype. Here, via RNA-seq of 240 different lettuce accessions, the authors identify loci and expression patterns associated with flavonoid and anthocyanin content and show that cultivated lettuce likely arose via a single domestication event