Search

A covariate-driven random coefficient generalized conditional heteroscedasticity (GARCH) time series model with the form of the buffered autoregression (BRC-GARCH-X) for modeling financial time series data is considered. As an extension of the classical two-regime threshold process, the buffered autoregression enjoys a more flexible regime-switching mechanism. Furthermore, the main feature of this model is that the threshold variable for regime-switching is formulated as a weighted average of important auxiliary variables. The estimator for regression parameters is obtained by the quasi-maximum exponential likelihood (QMEL) estimator and the corresponding asymptotic properties are established. Moreover, a mixed portmanteau test is developed for diagnostic checking. And a reasonable method for selecting search ranges for thresholds is also proposed and simulation studies are considered. As an application, we bring attention to some features of of stock returns of SP500 which shows that our model is feasible. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a new bivariate random coefficient integer-valued autoregressive process based on modified negative binomial operator with dependent innovations is proposed. Basic probabilistic and statistical properties of this model are derived. To estimate unknown parameters, Yule-Walker, conditional least squares and conditional maximum likelihood methods are considered and evaluated by Monte Carlo simulations. Asymptotic properties of the estimators are derived. Moreover, coherent forecasting and possible extension of the proposed model is provided. Finally, the proposed model is applied to the monthly crime datasets and compared with other models. [ABSTRACT FROM AUTHOR]

In this paper, a new threshold INAR(1) model based on modified negative binomial operator with random coefficient is proposed. Basic probabilistic and statistical properties of this process are established. Then the conditional least squares (CLS) and the conditional maximum likelihood (CML) methods are applied to estimate the model parameters when the threshold value is known or not. The asymptotic properties of the CLS-estimator and the CML-estimator are also been discussed. A method to test the constancy of the autoregressive parameters is provided. As an illustration, a simulation study is conducted to illustrate the performances of these estimators and present an empirical analysis of monthly counts of break and enter non-dwelling in Bellingen. [ABSTRACT FROM AUTHOR]

The random coefficient autoregressive (RCAR) processes are very useful to model time series in applications. It is commonly observed that the random autoregressive coefficient is assumed to be an independent identically distributed (i.i.d.) random variable sequence. To make the RCAR model more practical, this paper considers a new RCAR(1) model driven by explanatory variable and observations. We use the conditional least squares, the quantile regression and the conditional maximum likelihood methods to estimate the model parameters. The consistency and asymptotic normality of the proposed estimates are established. Simulation studies are conducted for the evaluation of the developed approaches and two applications to real-data examples are provided. The results show that the proposed procedures perform well for the simulations and application. [ABSTRACT FROM AUTHOR]

In this article, a new mixture integer-valued autoregressive process model with a finite random range Nt based on Pegram operator and binomial thinning operator is proposed. The new model can be well used to describe finite-range { 0, 1, ⋯ , L } integer-valued data with a broad variety of dispersion patterns, zero-inflation and multimodality. We derive the basic numerical characteristics of the model and testify stationarity and ergodicity properties. Conditional least squares method and maximum quasi-likelihood method are employed to estimate the parameters of NMBAR(1) model. The asymptotic properties of the estimators are acquired. Multiple groups of stochastic simulation studies are carried out to assess and compare the performances of these estimators and a real example is analyzed to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]

In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005. [ABSTRACT FROM AUTHOR]

This paper brings forward a pth-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (MDDRCINAR(p)). Stationarity and ergodicity properties of the proposed model are derived. The unknown parameters are estimated by conditional least squares, weighted least squares and maximum quasi-likelihood and asymptotic characterization of the obtained parameter estimators is proved. The performances of the proposed estimate methods are checked via simulations, which present that maximum quasi-likelihood estimators perform better than the other two estimate methods considering the proportion of within-Ω estimates in certain regions of the parameter space. The applicability of the model is investigated using two real count data sets. [ABSTRACT FROM AUTHOR]

This paper studies the empirical likelihood method for a multivariate first-order random coefficient autoregressive (RCAR(1)) model. Based on the modified least square score equation, empirical likelihood method is used to test the randomness of coefficients and estimate the unknown parameters. First, the limiting distribution of the log empirical likelihood ratio statistic is established. Second, the maximum empirical likelihood estimator is derived and its asymptotic properties are established. Finally, the performances of the estimator are compared with the modified least square estimator via simulation. Furthermore, simulation result shows that the test has the correct size and very good power for all cases considered. [ABSTRACT FROM AUTHOR]

Count data with zero truncation are common in the production process. It's essential to monitor these data during production flow, production quality control and market management. Most of the previous studies were based on the independent observations assumption. In fact, serial dependence of count data which significantly affects the performance of the control charts exists extensively in practice. Motivated by this, several important first-order integer-valued autoregressive time series processes are used to model the autocorrelated count data with zero truncation. We investigate the effectiveness of three following charts, the combined jumps chart, the exponentially weighted moving average chart and the cumulative sum chart, to detect the upward shifts of the process mean based on these models. A bivariate Markov chain approach could be used to obtain the average run length of these charts. Design recommendations for achieving robustness are provided based on the computation study. An application to product quality complaints data is presented to demonstrate good performances of the charts. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented. [ABSTRACT FROM AUTHOR]

Integer-valued time series data are greatly useful in many applications, such as reporting the daily number of patients impacted by an epidemic. When modelling high-order integer-valued time series data, order selection is a difficult task. In this paper, we propose a penalized empirical likelihood (PEL) method for order selection and parameter estimation in generalized pth-order integer-valued autoregressive (GINAR(p)) model. We show that the PEL method in the GINAR(p) model has oracle properties, which means that the PEL estimators identify the model as efficiently as if the true structure of the model was known ahead of time. Furthermore, we present the PEL ratio statistic to test a linear hypothesis of the parameter and demonstrate that it has an asymptotically χ 2 distribution under the null hypothesis. Numerical simulation and real data analysis are carried out to assess the performance our proposed method. [ABSTRACT FROM AUTHOR]

A class of integer-valued autoregressive models is considered, which are based on a latten process e.g., a Markov chain. Some statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. A Monte Carlo simulation study is conducted to examine the finite sample performance of the given estimation procedure. Finally, the performance of these models is illustrated by empirical applications to two sets of real counts. [ABSTRACT FROM AUTHOR]

To analyse count time series data inflated at the r + 1 values { 0 , 1 , ... , r } , we propose a new first-order integer-valued autoregressive process with r-geometrically inflated Poisson innovations. Some statistical properties together with conditional maximum likelihood estimate are provided. For the purpose of statistical monitoring, we focus on the cumulative sum chart, exponentially weighted moving average chart and combined jumps chart towards the proposed process. Numerical simulations indicate that the conditional maximum likelihood estimator is unbiased. Moreover, the cumulative sum chart is the best choice to monitor our model in practice. Some applications about telephone complaints data are provided to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the novel estimating methods and their properties for pth-order dependence-driven random coefficient integer-valued autoregressive time series model (DDRCINAR(p)) are studied as the innovation sequence has a Poisson distribution and the thinning is binomial. Strict stationarity and ergodicity for DDRCINAR(p) model are proved. Conditional maximum likelihood and conditional least squares are used to estimate the model parameters. Asymptotic normality of the proposed estimators are derived. Finite sample properties of the conditional maximum likelihood estimator are examined in relation to the widely used conditional least squares estimator. It is concluded that, if the Poisson assumption can be justified, conditional maximum likelihood method performs better in terms of bias and MSE. Finally, three real data sets are analyzed to demonstrate the practical relevance of the model. [ABSTRACT FROM AUTHOR]

To better describe the characteristics of time series of counts such as overdispersion or structural change, in this paper, we redefines the integer-valued threshold autoregressive models based on negative binomial thinning (NBTINAR(1)) under a weaker condition that the expectation of the innovations is finite. Parameters' point estimation and interval estimation problems are considered. A method to test the nonlinearity of the data is provided. As an illustration, we conduct a simulation study and empirical analysis of Pittsburgh crime data sets. [ABSTRACT FROM AUTHOR]

In this article, we generalize the individual risk model by introducing a dependence relationship between premiums and claim sizes. The dependence structure is described by bivariate distribution and copulas. Some statistical properties of the models are obtained. The loss probability, value at risk (VaR) and tail value at risk (TVaR) are applied to quantify the risk of the models. To this end, we use net loss (claim sizes minus premiums) instead of claim sizes to calculate the risk measures. Some numerical examples are given to illustrate the results. The normal approximation method is used to simplify the risk measures for the models. [ABSTRACT FROM AUTHOR]

Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, we introduce an extended binomial AR(1) model based on the generalized binomial thinning operator. This operator relaxes the independence assumption of the binomial thinning operator and contains dependent Bernoulli counting series. The new model contains the binomial AR(1) model as a particular case. Some probabilistic and statistical properties are explored. Estimators of the model parameters are derived by conditional maximum likelihood (CML), conditional least squares (CLS) and weighted conditional least squares (WCLS) methods. Some asymptotic properties and numerical results of the estimators are studied. The good performance of the new model is illustrated, among other competitive models in the literature, by an application to the monthly drunken driving counts. [ABSTRACT FROM AUTHOR]

To better describe the data whose variance is greater than mean in time series analysis, this paper introduces the RCINAR(1) process with generalized negative binomial marginals. The related estimations of this process are considered using Yule-Walker, modified conditional least squares, conditional maximum likelihood and Bayesian methods. The asymptotic properties of the estimators are established. Some simulations are conducted to verify the proposed estimation methods and a real example is proposed to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]

Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process of serially dependent counts with deflation or inflation of zeros is commonly observed in many applications. This paper investigates the monitoring of such a process, the first-order zero-modified geometric integer-valued autoregressive process (ZMGINAR(1)). In particular, two control charts, the upper-sided and lower-sided CUSUM charts, are developed to detect the shifts in the mean process of the ZMGINAR(1). Both the average run length performance and the standard deviation of the run length performance of these two charts are investigated by using Markov chain approaches. Also, an extensive simulation is conducted to assess the effectiveness or performance of the charts, and the presented methods are applied to two sets of real data arising from a study on the drug use. [ABSTRACT FROM AUTHOR]

This article studies the threshold autoregression analysis for the self-exciting threshold binomial autoregressive processes. Parameters' point estimation and interval estimation problems are considered via the empirical likelihood method. A new algorithm to estimate the threshold value of the threshold model is also given. Simulation study is conducted for the evaluation of the developed approach. An application on measles data is provided to show the applicability of the method. [ABSTRACT FROM AUTHOR]

In this article, we develop a Bayesian analysis in autoregressive model with explanatory variables. When σ2is known, we consider a normal prior and give the Bayesian estimator for the regression coefficients of the model. For the case σ2is unknown, another Bayesian estimator is given for all unknown parameters under a conjugate prior. Bayesian model selection problem is also being considered under the double-exponential priors. By the convergence of ρ-mixing sequence, the consistency and asymptotic normality of the Bayesian estimators of the regression coefficients are proved. Simulation results indicate that our Bayesian estimators are not strongly dependent on the priors, and are robust. [ABSTRACT FROM PUBLISHER]

Conclusions: Most neoplasms of the parapharyngeal space are benign. The transcervical surgical approach is the preferred procedure in most cases. Intra-operative endoscopic exploration offers a new surgical management technique to reduce operative complications and post-operative recurrence rates. Objectives: To present the authors' experience in the treatment of primary parapharyngeal space tumors with emphasis on surgical approaches and employment of endoscopy to detect residual tumors through conventional approaches. Method: One hundred and sixty-seven patients treated surgically over a 10-year period were retrospectively reviewed. A comparison of the surgical approaches in a relevant case series was also conducted. Results: All of the patients underwent pre-operative imaging before surgery, and intra-operative endoscopic detection was selectively used for large, deep tumors. Complete resection of the tumor was used in 158 patients (95%), with a transcervical surgical approach applied in 144 cases (84%). Of 167 tumors, 150 (90%) were benign and 17 (10%) were malignant, with neurilemmoma/schwannoma as the most frequent pathology (42%). Surgical complications were reported in 26 patients (15%), most commonly unilateral paralysis of the vocal cords (6%). Two patients (1%) presented with recurrence, on average 2.5 years (range = 1–4 years) after initial excision, and the mean follow-up time was 3.8 years (range = 10 months–10 years). [ABSTRACT FROM AUTHOR]

In this article, the problem of interest is testing the conditional heteroscedasticity of Poisson autoregressive model. We construct a non parametric test statistic based on empirical likelihood method. The asymptotic distribution of the proposed statistic is derived and its finite-sample property is examined through Monte Carlo simulations. The simulation results show that the proposed method is good for practical use. [ABSTRACT FROM PUBLISHER]

We investigated the empirical likelihood inference approach under a general class of semiparametric hazards regression models with survival data subject to right-censoring. An empirical likelihood ratio for the full 2pregression parameters involved in the model is obtained. We showed that it converged weakly to a random variable which could be written as a weighted sum of 2pindependent chi-squared variables with one degree of freedom. Using this, we could construct a confidence region for parameters. We also suggested an adjusted version for the preceding statistic, whose limit followed a standard chi-squared distribution with 2pdegrees of freedom. [ABSTRACT FROM PUBLISHER]

In this paper, we proposed a class of tests of proportional hazards assumption for left-truncated and right-censored data based on a pair of estimators of the hazard ratio constant. Using counting process and martingale theory, the asymptotically normal distribution of the test statistic is derived and a family of consistent estimators of variance are also provided. Extensive simulation studies were conducted to evaluate the performance of the proposed test statistics under finite sample situations. Two real data sets are analyzed to illustrate our method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study a generalized algorithm for the most commonly used D-optimal algorithm in experimental design. We extend the classical D-optimal algorithm to the D-optimal assignment problem for regression models considering the cost of experiments. To solve the problem, we construct a weighted objective function and establish a generalized D-optimal (GD-optimal) criterion to make this objective function optimal. We propose a multiplicative algorithm to compute the GD-optimal allocation and provide some theorems to ensure that the algorithm converges to the GD-optimal. Numerical examples show that the proposed algorithm performs well. [ABSTRACT FROM AUTHOR]

Conclusion: This study provides evidence that histamine induced MUC5AC mRNA expression through the activation of the core region of its promoter. It may also help in approaching new therapeutic strategies in airway mucins hypersecretory diseases. Objective: Mucin hypersecretion characterizes several respiratory diseases. Production of MUC5AC, a major gel forming mucin secreted by airway epithelia, can be induced by various inflammatory mediators. Histamine is associated with MUC5AC up-regulation during the early phase of allergic respiratory diseases. The goal of the present study was to identify whether histamine may induce MUC5AC gene expression both at mRNA and protein levels and to elucidate its mechanism. Methods: Guinea pigs were sensitized and challenged with dermatophagoides farinae (Der f) extract. Human lung mucoepidermoid carcinoma cell line (NCI-H292) was used. The regulatory mechanism of MUC5AC by histamine and H1R was investigated using RT-PCR, immunofluorescence, and MUC5AC promoter-driven luciferase reporter assay. Results: The MUC5AC expression levels were increased by histamine treatment in either nasal tissues of Der f challenged guinea pigs or NCI-H292 cells, whereas the MUC5AC protein over-production induced by histamine administration was significantly inhibited by H1R antagonist chlorpheniramine. It was found that histamine enhanced the activation of the proximal core region of the MUC5AC promoter, which was significantly blocked by chlorpheniramine, as indicated by luciferase reporter assays. [ABSTRACT FROM AUTHOR]

In this article, we present the explicit expressions for the higher-order moments and cumulants of the first-order random coefficient integer-valued autoregressive (RCINAR(1)) process. The spectral and bispectral density functions are also obtained, which can characterize the RCINAR(1) process in the frequency domain. We use a frequency domain approach which is named Whittle criterion to estimate the parameters of the process. We propose a test statistic which is based on the frequency domain approach for the hypothesis test,H0: α = 0⟷H1: 0 < α < 1, where α is the mean of the random coefficient in the process. The asymptotic distribution of the test statistic is obtained. We compare the proposed test statistic with other statistics that can test serial dependence in time series of count via a typically numerical simulation, which indicates that our proposed test statistic has a good power. [ABSTRACT FROM AUTHOR]

Conclusion: This work demonstrated that juvenile nasopharyngeal angiofibromas (JNAs) express high levels of hormone receptors and vascular endothelial growth factor (VEGF) compared with normal nasal mucosa. The interaction between hormone receptors and VEGF may be involved in the initiation and growth of JNA. Objectives: JNA is a rare benign tumor that occurs almost exclusively in male adolescents. Although generally regarded as a hormone-dependent tumor, this has not been proven in previous studies. The aim of this study was to investigate the role of hormone receptors in JNA and the relationship with clinical characteristics. Methods: Standard immunohistochemical microarray analysis was performed on 70 JNA samples and 10 turbinate tissue samples. Specific antibodies for androgen receptor (AR), estrogen receptor-α (ER-α), estrogen receptor-β (ER-β), progesterone receptor (PR), and VEGF were examined, and the relationships of receptor expression with age, tumor stage, and bleeding were evaluated. Results: Results showed that JNA expressed ER-α (92.9%), ER-β (91.4%), AR (65.7%), PR (12.8%), and VEGF (95.7%) at different levels. High level of VEGF was linked to elevated ER-α and ER-β. There was no significant relationship between hormonal receptors and age at diagnosis, tumor stage or bleeding. However, overexpression of ER-α was found to be an indicator of poor prognosis ( p = 0.031). [ABSTRACT FROM AUTHOR]

Cossette et al. (2010, 2011) gave a novel collective risk model where the total numbers of claims satisfy the first-order integer-valued autoregressive process. For a risk model, it is interesting to investigate the upper bound of ruin probability. However, the loss increments of the above model are dependent; it is difficult to derive the upper bound of ruin probability. In this article, we propose an approximation model with stationary independent increments. The upper bound of ruin probability and the adjustment coefficient are derived. The approximation model is illustrated via four simulated examples. Results show that the gap of the approximation model and dependent model can be ignored by adjusting values of parameters. [ABSTRACT FROM PUBLISHER]

In this paper, we propose several approaches to estimate the parameters of the periodic first-order integer-valued autoregressive process with period T (PINAR(1)T) in the presence of missing data. By using incomplete data, we propose two approaches that are based on the conditional expectation and conditional likelihood to estimate the parameters of interest. Then we study three kinds of imputation methods for the missing data. The performances of these approaches are compared via simulations. [ABSTRACT FROM AUTHOR]

A generalized random coefficient first-order integer-valued autoregressive process with signed thinning operator is introduced, this kind of process is appropriate for modeling negative integer-valued time series. Strict stationarity and ergodicity of process are established. Estimators of the parameters of interest are derived and their properties are studied via simulation. At last, we use bootstrap method in the real data analysis. [ABSTRACT FROM AUTHOR]

This article studies the empirical likelihood method for the first-order random coefficient integer-valued autoregressive process. The limiting distribution of the log empirical likelihood ratio statistic is established. Confidence region for the parameter of interest and its coverage probabilities are given, and hypothesis testing is considered. The maximum empirical likelihood estimator for the parameter is derived and its asymptotic properties are established. The performances of the estimator are compared with the conditional least squares estimator via simulation. [ABSTRACT FROM AUTHOR]

Conclusion: The endoscopic extended medial maxillectomy approach for the management of lesions of the pterygopalatine and infratemporal fossa provides excellent exposure and results with good hemostasis and low morbidity. This approach is a viable alternative to the open approaches to these areas. Objectives: To describe an endoscopic extended medial maxillectomy approach for the treatment of nonmalignant tumors in the pterygopalatine and infratemporal fossa. Methods: From January 2004 to June 2007, five patients who had tumors in the pterygopalatine fossa and/or infratemporal fossa, and underwent surgical resection of the tumors with the endoscopic extended medial maxillectomy approach, were reviewed regarding demographics, preoperative images, tumor cell type, surgical techniques, and outcomes. Results: Five patients underwent the procedure mentioned above; three females and two males with a mean age of 38 and a range of 21-58 years. All patients had adequate exposure and total tumor resection with the endoscopic extended medial maxillectomy approach. None of the patients required an external approach for tumor extirpation. There were no major postoperative complications. No evidence of tumor recurrence was noted after follow-up for 12-78 months. [ABSTRACT FROM AUTHOR]

We propose a new integer-valued time series process, called generalized pth-order random coefficient integer-valued autoregressive process with signed thinning operator. This kind of process is appropriate for modeling negative integer-valued time series; strict stationarity and ergodicity of the process are established. Estimators of the model's parameters are derived and their properties are studied via simulation. We apply our process to a real data example. [ABSTRACT FROM AUTHOR]

Conclusion: Collagen deposition was increased in regenerative rabbit maxillary mucosa, and the elevated collagen deposition during wound healing was associated with increased interleukin (IL)-6 and IL-8, and decreased IL-10. Objectives: To observe the natural progression of wound healing in rabbit maxillary sinus mucosa and to detect the expression of relevant cytokines. Methods: A rabbit wound-healing model was established in the maxillary sinus. At different times after injury, the sizes of maxillary ostia were recorded, and histological changes in the sinus mucosa were examined by means of hematoxylin and eosin (H&E) and Masson trichrome staining. Enzyme-linked immunosorbent assay (ELISA) was used to detect the concentrations of IL-6, IL-8, IL-10, and collagen types I and III. Results: A large amount of immature spindled collagen deposition was observed in the basal lamina and lamina propria of regenerative mucosa. IL-6 was increased significantly in regenerative mucosa after injury, especially at 14 days post wounding when compared with normal mucosa. IL-8 was also increased significantly in regenerative mucosa; however, IL-10 was decreased significantly when compared with normal mucosa. IL-6 and IL-8 showed a significant correlation with the expression level of collagen type I. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of estimating the volatility function of a parametric AR(1) model with nonparametric ARCH(1) errors. Consistency and asymptotic normality of local constant and local log-linear estimators are established. Our simulation study and an application to finance lead to superior performance of the local log-linear estimator compared with the conventional treatments in the literature. A possible extension of the estimation procedure is described. [ABSTRACT FROM AUTHOR]

Conclusion: The data revealed that calcium influx via the NMDA receptor up-regulated the expression of phosphorylated c-Jun in the primary auditory cortices following sensory stimulation and after different neural injury stimulations which guide activity-dependent changes in gene expression and neural plasticity. Objectives: Activator protein-1 (AP-1) transcription factor, which is mainly composed of c-Fos and c-Jun proteins, is believed to be a key participant in molecular processes that guide activity-dependent changes in gene expression. Our previous study had shown that the expression of NMDAR2A gene on synaptosomes membrane of auditory cortical neurons varied by electrical intracochlear stimulation (EIS) and neural injury induced by acoustic trauma. In this study, we investigated the role of the NMDA receptor (NMDAR) in regulating the expression of phosphorylated c-Jun in the primary auditory cortex (AI). The modulation factors observed for gene expression included EIS and noise traumas. Materials and methods: EIS was applied in rats with early postnatal auditory deprivation. The impact of the noise traumas on the ultrastructures of spiral ganglion neurons (SGNs) and their innervations to inner hair cells (IHCs) were verified by transmission electron microscopy (EM). These changes include a decrease in subcellular organelles, the swelling of mitochondria and endoplasmic reticulum, the morphological changes in cell nuclei, and damage in the afferent synapse. Results: Immunohistochemistry observations showed that the expression of phosphorylated c-jun and active caspase-3 in hair cells and SGNs varied with amount of noise. Immunocytochemistry and Western blotting showed that the auditory cortex began to express phosphorylated c-jun 24 h after 2 h of EIS. However, this expression was not changed by EIS if NMDAR antagonist was applied. The level of phosphorylated c-Jun was remarkably increased in AI after noise overstimulation at 115 dB SPL for 3 h. Again, such an increase was not seen if NMDAR antagonist 3-(2 carboxypiperazin-4yl) propyl-1-phosphonic acid (CPP, 10 mg/kg, i.p.) was applied 30 min before the noise exposure. [ABSTRACT FROM AUTHOR]

Conclusions. The expression of synaptic N-methyl-D-aspartate (NMDA) receptors in the auditory cortex is dynamic and is bidirectionally regulated by auditory activity. Furthermore, the time course of changes in the level of NR2A protein differs after sensory and neuronal injury stimulation, which modulate different changes in synaptic plasticity. Objective. To examine the effects of different types of auditory activity on the expression of synaptic NMDA receptors (NMDARs) in the auditory cortex of rats. Material and methods. We prepared synaptosomes from the auditory cortices of postnatal Day 28 ototoxic-deafened Sprague-Dawley rats and postnatal Day 28 Sprague-Dawley rats subjected to noise trauma that were given various treatments and compared them to the synaptosomes of 1-6-week-old normal Sprague-Dawley rats. The expression of different NMDAR subunits in the synaptosomes was investigated by means of Western blotting. Results. Changes in NRI and NR2B proteins were not significant during different types of auditory activity. The level of NR2A protein increased remarkably during postnatal development and as a result of electrical intracochlear stimulation, auditory deprivation and noise trauma. Seventy-two h after a 2-h period of sensory electrical intracochlear stimulation, the expression of NR2A protein returned to the level caused by auditory deprivation. Seventy-two h after a 3-h period of noise trauma, elevation of the level of NR2A protein was unchanged. We also confirmed that elevation of the level of synaptic NR2A protein was sensitive to protein synthesis inhibitor and NMDAR antagonist. However, transcription inhibitor had no effect on NR2A protein expression. [ABSTRACT FROM AUTHOR]

In the article, we consider a stationary autoregressive conditional heteroscedastic (ARCH) process of the form: X[SUBt] = &xgr;[SUBt]h[SUBtSUPfrac12], h[SUBt] = &alpha[SUB0]+α[SUB1]X[SUBt-1SUP2] with P(&xgr;[SUB1SUP2] ≤ &xgr;[SUBt]h[SUBtSUPfrac12] ≤ &xgr;(1/2), where &xgr;(1/2) is a known constant independent of the parameters α[SUB0] and α[SUB1]. This article proposes a method to obtain the estimator of the parameters and shows that the estimator is a median-unbiased. Some results comparing the proposed estimator with the usual maximum likelihood estimator of the parameters are also given in this article. [ABSTRACT FROM AUTHOR]

This paper considers the estimation of parameters based on a progressively type-I interval-censored data from a mixed generalized exponential distribution. The maximum likelihood estimation is used but an analytic form cannot be obtained. The EM algorithm is applied to obtain the maximum likelihood estimates. The performance of the estimates is judged by a simulating study and a real data is presented to illustrate the method of estimation developed here. [ABSTRACT FROM AUTHOR]