双导丝法联合胰管支架置入术在胆总管结石ERCP中的应用研究

目的研究双导丝法联合胰管支架置入术在胆总管结石内镜逆行胰胆管造影术(ERCP)中的应用效果。方法回顾性分析本院2020年1月至2022年1月期间接诊的106例胆总管结石患者的临床资料。根据不同的治疗方案将其分为研究组与对照组各53例。两组患者均行ERCP,对照组采用传统胆管选择性插管进行治疗,研究组采用双导丝法联合胰管支架置入术进行治疗。记录并比较两组患者的插管成功率、手术时间、插管时间、术后进食时间、住院时间及术后并发症发生率。结果研究组患者的插管成功率为94.34%,明显高于对照组患者的77.36%(P<0.05);两组患者的术后进食时间相比(P>0.05),研究组患者的手术时间、插管时间、住院时间均明显短于对照组(均P<0.05);对照组与研究组患者术后并发症发生率分别为15.09%、7.55%,两组差异无统计学意义(P>0.05)。结论双导丝法联合胰管支架置入术在胆总管结石ERCP中的应用效果显著,能有效地提高患者的插管率,缩短手术时间、插管时间及住院时间,且安全性高,具有临床应用价值。

关于当前概率统计教材与教学的一些思考

本文针对当前概率统计教材与教学提出了一些思考.

Local linear estimator for stochastic diferential equations driven by α-stable Lvy motions

We study tile local linear estimator for tile drift coefficient of stochastic differential equations driven by α-stable Levy motions observed at discrete instants. Under regular conditions, we derive the weak consis- tency and central limit theorem of the estimator. Compared with Nadaraya-Watson estimator, the local linear estimator has a bias reduction whether the kernel function is symmetric or not under different schemes. A silnu- lation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator, especially on the boundary.

Empirical likelihood inference for diffusion processes with jumps

In this paper, we consider the empirical likelihood inference for the jump-diffusion model. We construct the confidence intervals based on the empirical likelihood for the infinitesimal moments in the jump-diffusion models. They are better than the confidence intervals which are based on the asymptotic normality of point estimates.

Reweighted Nadaraya-Watson estimation of jump-diffusion models

In this paper,we study the nonparametric estimation of the second infinitesimal moment by using the reweighted Nadaraya-Watson (RNW) approach of the underlying jump diffusion model.We establish strong consistency and asymptotic normality for the estimate of the second infinitesimal moment of continuous time models using the reweighted Nadaraya-Watson estimator to the true function.

Consistency of kernel density estimators for causal processes

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated.First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean(and the true density function)are derived.Secondly,the corresponding strong convergence rates are investigated.It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d.density models are also optimal for these processes.

Nonparametric estimation of quantiles for a class of stationary processes

We study smoothed quantile estimator for a class of stationary processes. We obtain the convergency rates and the Bahadur representation, as well as the asymptotic normality for this estimator by the method of m-dependent approximation. Our results can be used in the study of the estimation of value-at-risk(Va R) and applied to many time series which have important applications in econometrics.

The invariance principle for fractionally integrated processes with strong near-epoch dependent innovations

In this paper, we show the invariance principle for the partial sum processes of fractionally integrated processes, otherwise known as I(d + m) processes, where |d| < 1/2 and m is a nonnegative integer, with strong near-epoch dependent innovations. The results are applied to the test of unit root. The conditions given improve previous results in the literature concerning fractionally integrated processes.

ON LARGE INCREMENTS OF l^p-VALUED GAUSSIAN PROCESSES

Let{X k(t),t≥0},k=1,2,…,be a sequence of independent Gaussian processes withσk 2(h)=E(X k(t+h)-X k(t))2.Putσ(p,h)=(∑∞k=1σk p(h))1/p,p≥1.The author establishes the large increment results for boundedσ(p,h).

CONVERGENCE ON RANDOMLY TRIMMED SUMS WITH A DEPENDENT SAMPLE

Let{X n}be a sequence of random variables and X n1X n2…X nn their order statistics.In this paper a central limit theorem and a strong law of large numbers for randomly trimmed sums T n=βn i=αn+1 X ni are established in the case thatαn andβn are positive integer-valued random variables such thatαn/n andβn/n converge to random variablesαandβrespectively with 0α<β1 in certain sense,and{X n}is aφ-mixing sequence.