In this paper, we are concerned with the problem of the pathwise uniqueness of one-dimensional reflected stochastic differential equations with jumps under the assumption of non-Lipschitz continuous coefficients whose...
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In this paper, we are concerned with the problem of the pathwise uniqueness of one-dimensional reflected stochastic differential equations with jumps under the assumption of non-Lipschitz continuous coefficients whose proof are based on the technique of local time.
This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV *** first establish the consistency and asymptotic normality of the conditional least squares...
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This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV *** first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant *** semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.
A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root *** this paper,we consider the unit root testing problem with errors being long-...
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A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root *** this paper,we consider the unit root testing problem with errors being long-memory processes with the LARCH structure.A new test statistic is developed by using the random weighted bootstrap *** turns out that the proposed statistic has a chisquared distribution asymptotically regardless of the process being stationary or nonst at ionary,and with or without an intercept *** simulation results show that the statistic has a desired finite sample performance in terms of both size and power.A real data application is also given relying on the inflation rate data of 17 countries.
In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful *** address the high-dimensional issue of these applications,it is common to assume some sparsity *** the c...
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In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful *** address the high-dimensional issue of these applications,it is common to assume some sparsity *** the case of the parameter matrix being simultaneously low rank and elements-wise sparse,we estimate the parameter matrix through the least-squares approach with the composite penalty combining the nuclear norm and the *** extend the existing analysis of the low-rank trace regression with *** to exponentialβ-mixing *** explicit convergence rate and the asymptotic properties of the proposed estimator are ***,as well as a real data application,are also carried out for illustration.
Portmanteau tests have drawn much interest in economics and finance because of their strong relationship to model specification. The majority of current testing, however, concentrates on stationary time series. This a...
Portmanteau tests have drawn much interest in economics and finance because of their strong relationship to model specification. The majority of current testing, however, concentrates on stationary time series. This article proposes an empirical likelihood-based portmanteau test for the autoregressive model, no matter if it is stationary, nearly integrated, or unit root, and with or without an intercept. It turns out that the final statistic is always asymptotically chi-squared distributed. A simulation study confirms the good finite sample performance of the proposed test before illustrating its practical merit in analyzing real data sets.
Conditional dependence plays a crucial role in various statistical procedures, including variable selection, network analysis and causal inference. However, there remains a paucity of relevant research in the context ...
Conditional dependence plays a crucial role in various statistical procedures, including variable selection, network analysis and causal inference. However, there remains a paucity of relevant research in the context of high-dimensional conditioning variables, a common challenge encountered in the era of big data. To address this issue, many existing studies impose certain model structures, yet high-dimensional conditioning variables often introduce spurious correlations in these models. In this paper, we systematically study the estimation biases inherent in widely-used measures of conditional dependence when spurious variables are present under high-dimensional settings. We discuss the estimation inconsistency both intuitively and theoretically,demonstrating that the conditional dependencies can be either overestimated or underestimated under different scenarios. To mitigate these biases and attain consistency, we introduce a measure based on data splitting and refitting techniques for high-dimensional conditional dependence. A conditional independence test is also developed using the newly advocated measure, with a tuning-free asymptotic null distribution. Furthermore,the proposed test is applied to generating high-dimensional network graphs in graphical modeling. The superior performances of newly proposed methods are illustrated both theoretically and through simulation studies. We also utilize the method to construct the gene-gene networks using a dataset of breast invasive carcinoma, which contains interesting discoveries that are worth further scientific exploration.
Tensor data have been widely used in many fields,e.g.,modern biomedical imaging,chemometrics,and economics,but often suffer from some common issues as in high dimensional *** to find their low-dimensional latent struc...
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Tensor data have been widely used in many fields,e.g.,modern biomedical imaging,chemometrics,and economics,but often suffer from some common issues as in high dimensional *** to find their low-dimensional latent structure has been of great interest for *** this end,we develop two efficient tensor sufficient dimension reduction methods based on the sliced average variance estimation(SAVE)to estimate the corresponding dimension reduction *** first one,entitled tensor sliced average variance estimation(TSAVE),works well when the response is discrete or takes finite values,but is not■consistent for continuous response;the second one,named bias-correction tensor sliced average variance estimation(CTSAVE),is a de-biased version of the TSAVE *** asymptotic properties of both methods are derived under mild *** and real data examples are also provided to show the superiority of the efficiency of the developed methods.
In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking ...
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In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in *** the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also *** derive the asymptotic properties of the related statistics under mild *** simulations,as well as a real data example,are conducted to illustrate the finite sample performance.
The paper presents a method to obtain Pythagorean fuzzy information (PFI) based on a bidirectional long short-Term memory (BiLSTM) neural network. The method first uses the Word2Vec word embedding model for vectorized...
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Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding o...
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Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.
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