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Objectives: To explore the prognostic factors in patients with advanced olfactory neuroblastoma (ONB) underwent endoscopic surgery. Materials and Methods: Retrospective medical records were reviewed of patients with pathologically proven ONB who underwent endoscopic surgical resection. Clinicopathological characteristics including patient demographics, treatment, complications, follow-up, and outcomes were analyzed. Kaplan–Meier overall survival (OS) and disease-free survival (DFS) curves were plotted. Univariate and multivariate Cox regression models were used to determine prognostic factors. Results: Eighty-five patients with Kadish stage C ONB were examined. According to the various staging systems used, most patients harbored modified Kadish stage C (78.8%). Twenty-six patients (30.6%) underwent bony skull base resection, 11 (12.9%) underwent dura resection, and 24 (28.2%) underwent additional intracranial resection that included the olfactory bulb and duct. Median follow-up was 39 months. Five-year OS and DFS rates were 83.7% and 74.9%, respectively. Five-year OS was 100% in patients treated with bony skull base resection and 77.5% in those who were not (P =.052). Dura resection did not improve OS. Multivariate Cox regression analysis identified perioperative complications (P =.009), gross total resection (P =.004), orbital invasion (P =.014), postoperative radiotherapy (P =.030), and bony skull base resection (P =.019) as independent prognostic predictors. Conclusion: For patients with advanced ONB, endoscopic surgery in conjunction with radiotherapy and chemotherapy is effective and safe. Dura resection should be performed with caution in selected patients to balance survival and complications. Postoperative radiotherapy is important to improve OS and DFS. [ABSTRACT FROM AUTHOR]

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]

While overdispersion is a common phenomenon in univariate count time series data, its exploration within bivariate contexts remains limited. To fill this gap, we propose a bivariate integer-valued autoregressive model. The model leverages a modified binomial thinning operator with a dispersion parameter ρ and integrates random coefficients. This approach combines characteristics from both binomial and negative binomial thinning operators, thereby offering a flexible framework capable of generating counting series exhibiting equidispersion, overdispersion, or underdispersion. Notably, our model includes two distinct classes of first-order bivariate geometric integer-valued autoregressive models: one class employs binomial thinning (BVGINAR(1)), and the other adopts negative binomial thinning (BVNGINAR(1)). We establish the stationarity and ergodicity of the model and estimate its parameters using a combination of the Yule–Walker (YW) and conditional maximum likelihood (CML) methods. Furthermore, Monte Carlo simulation experiments are conducted to evaluate the finite sample performances of the proposed estimators across various parameter configurations, and the Anderson-Darling (AD) test is employed to assess the asymptotic normality of the estimators under large sample sizes. Ultimately, we highlight the practical applicability of the examined model by analyzing two real-world datasets on crime counts in New South Wales (NSW) and comparing its performance with other popular overdispersed BINAR(1) models. [ABSTRACT FROM AUTHOR]

Summary: Data related to the counting of elements of variable character are frequently encountered in time series studies. This paper brings forward a new class of k$$ k $$th‐order dependence‐driven random coefficient mixed thinning integer‐valued autoregressive time series model (DDRCMTINAR(k$$ k $$)) to deal with such data. Stationarity and ergodicity properties of the proposed model are derived in detail. The unknown parameters are estimated by conditional least squares, and modified quasi‐likelihood and asymptotic normality of the obtained parameter estimators is established. The performances of the adopted estimate methods are checked via simulations, which present that modified quasi‐likelihood estimators perform better than the conditional least squares considering the proportion of within‐Ω$$ \Omega $$ estimates in certain regions of the parameter space. The validity and practical utility of the model are investigated by epileptic seizure data and COVID‐19 data of suspected cases in China. [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 application of seawater and sea sand concrete (SWSSC) can reduce the construction period and cost of island infrastructure, but it may also bring the risk of alkali–silica reaction (ASR) owing to the presence of alkali ions in seawater and sea sand. To compare the characteristics of ASR between SWSSC and seawater and desalinated sea sand (DSS) concrete, the properties and the ASR products of mortar bars with different DSS content were studied. When the DSS proportion increased from 0% to 100%, the sodium (Na+), potassium (K+) and calcium (Ca2+) ion concentration of the specimens decreased by 22.6, 2.0 and 45.1 mg L−1, respectively, the pH decreased by 0.05 and the expansion of mortar bars was reduced by 0.16%. Desalination of sea sand could not eliminate the risk of ASR of SWSSC completely. The 14 days expansion of mortar bars with 100% DSS was 0.13%, and the precursors of ASR-P1 were observed by SEM. The experimental results of micriscopic tests all showed that with the increase of DSS proportion, the content of Na-shlykovite and ASR-P1 were reduced. A small amount of magnesium (Mg) in ASR products was detected. This study can provide a basis for the application of SWSSC in island infrastructure. [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]