Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown

This paper proposes a semiparametric method for estimating duration models when there are inequality constraints on some parameters and the error distribution may be unknown. Thus, the setting considered here is particularly suitable for practical applications. The parameters in duration models are usually estimated by a quasi-MLE. Recent advances show that a semiparametrically efficient estimator [SPE] has better asymptotic optimality properties than the QMLE provided that the parameter space is unrestricted. However, in several important duration models, the parameter space is restricted, for example in the commonly used linear duration model some parameters are non-negative. In such cases, the SPE may turn out to be outside the allowed parameter space and hence are unsuitable for use. To overcome this difficulty, we propose a new constrained semiparametric estimator. In a simulation study involving duration models with inequality constraints on parameters, the new estimator proposed in this paper performed better than its competitors. An empirical example is provided to illustrate the application of the new constrained semiparametric estimator and to show how it overcomes difficulties encountered when the unconstrained estimator of nonnegative parameters turn out to be negative.Adaptive inference; Conditional duration model; Constrained inference; Efficient semiparametric estimation; Order restricted inference; Semiparametric efficiency bound.

Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown

The parameters in duration models are usually estimated by a Quasi Maximum Likelihood Estimator [QMLE]. This estimator is efficient if the errors are iid and exponentially distributed. Otherwise, it may not be the most efficient. Motivated by this, a class of estimators has been introduced by Drost and Werker (2004). Their estimator is asymptotically most efficient when the error distribution is unknown. However, the practical relevance of their method remains to be evaluated. Further, although some parameters in several common duration models are known to be nonnegative, this estimator may turn out to be negative. This paper addresses these two issues. We propose a new semiparametric estimator when there are inequality constraints on parameters, and a simulation study evaluates the two semiparametric estimators. The results lead us to conclude the following when the error distribution is unknown: (i) If there are no inequality constraints on parameters then the Drost-Werker estimator is better than the QMLE, and (ii) if there are inequality constraints on parameters then the estimator proposed in this paper is better than the Drost-Werker estimator and the QMLE. In conclusion, this paper recommends estimators that are better than the often used QMLE for estimating duration models

Progress in analgesia for labor: focus on neuraxial blocks

Neuraxial analgesia is widely accepted as the most effective and the least depressant method of providing pain relief in labor. Over the last several decades neuraxial labor analgesia techniques and medications have progressed to the point now where they provide high quality pain relief with minimal side effects to both the mother and the fetus while maximizing the maternal autonomy possible for the parturient receiving neuraxial analgesia. The introduction of the combined spinal epidural technique for labor has allowed for the rapid onset of analgesia with minimal motor blockade, therefore allowing the comfortable parturient to ambulate. Patient-controlled epidural analgesia techniques have evolved to allow for more flexible analgesia that is tailored to the individual needs of the parturient and effective throughout the different phases of labor. Computer integrated systems have been studied to provide seamless analgesia from induction of neuraxial block to delivery. New adjuvant drugs that improve the effectiveness of neuraxial labor analgesia while decreasing the side effects that may occur due to high dose of a single drug are likely to be added to future labor analgesia practice. Bupivacaine still remains a popular choice of local anesthetic for labor analgesia. New local anesthetics with less cardiotoxicity have been introduced, but their cost effectiveness in the current labor analgesia practice has been questioned

Stock Market Measures and Market Performance

Sri Lanka is considered a highly fluctuating economy in the South Asian region. It is vital to understand the behavior of economy in order to obtain the maximum benefit. Stock market can be considered as one of the key influencers to the economy whereas the behavior of the stock market would highly define the behaviors of the overall economic system. It is required to identify the stock market measures and their contribution for the market development in order to identify the influence of stock market.The immense importance of its actions on the market performance leads to find more about the stock market’s measures. This research contains the evidence of the study conducted to identify the development of the stock market along with the behavior of the stock market measures such as all share price index, market capitalization, dividend yield, price to earnings ratio and shares traded equity.  All of these variables were used to obtain a model to describe and predict performance of stock market over the time.This study is based on the secondary data obtained from the CSE (Colombo Stock Exchange). A trend analysis was conducted for each series of data and results were used for the analysis carried on from there. Unit root test was performed to ensure the stationarity of the data. Then, a time series regression model and Granger causality tests were used to identify the relationship between the measures of stock market. Major finding of the study depicts that all the measures of the stock market have influences on the stock market development except for the dividend yield. These findings are useful in the process of decision making in many aspects.

Cardiac Output Assessed by Invasive and Minimally Invasive Techniques

Cardiac output (CO) measurement has long been considered essential to the assessment and guidance of therapeutic decisions in critically ill patients and for patients undergoing certain high-risk surgeries. Despite controversies, complications and inherent errors in measurement, pulmonary artery catheter (PAC) continuous and intermittent bolus techniques of CO measurement continue to be the gold standard. Newer techniques provide less invasive alternatives; however, currently available monitors are unable to provide central circulation pressures or true mixed venous saturations. Esophageal Doppler and pulse contour monitors can predict fluid responsiveness and have been shown to decrease postoperative morbidity. Many minimally invasive techniques continue to suffer from decreased accuracy and reliability under periods of hemodynamic instability, and so few have reached the level of interchangeability with the PAC

A Monocular Indoor Localiser Based on an Extended Kalman Filter and Edge Images from a Convolutional Neural Network

© 2018 IEEE. The main contribution of this paper is an extended Kalman filter (EKF)based algorithm for estimating the 6 DOF pose of a camera using monocular images of an indoor environment. In contrast to popular visual simultaneous localisation and mapping algorithms, the technique proposed relies on a pre-built map represented as an unsigned distance function of the ground plane edges. Images from the camera are processed using a Convolutional Neural Network (CNN)to extract a ground plane edge image. Pixels that belong to these edges are used in the observation equation of the EKF to estimate the camera location. Use of the CNN makes it possible to extract ground plane edges under significant changes to scene illumination. The EKF framework lends itself to use of a suitable motion model, fusing information from any other sensors such as wheel encoders or inertial measurement units, if available, and rejecting spurious observations. A series of experiments are presented to demonstrate the effectiveness of the proposed technique