26 research outputs found
Sequential procedures for nonparametric kernel regression
No full textIn a nonparametric setting, the functional form of the relationship between the response variable and the associated predictor variables is unspecified; however it is assumed to be a smooth function. The main aim of nonparametric regression is to highlight an important structure in data without any assumptions about the shape of an underlying regression function. In regression, the random and fixed design models should be distinguished. Among the variety of nonparametric regression estimators currently in use, kernel type estimators are most popular. Kernel type estimators provide a flexible class of nonparametric procedures by estimating unknown function as a weighted average using a kernel function. The bandwidth which determines the influence of the kernel has to be adapted to any kernel type estimator. Our focus is on Nadaraya-Watson estimator and Local Linear estimator whic h belong to a class of kernel type regression estimators called local polynomial kernel estimators.A closely related problem is the determination of an appropriate sample size that would be required to achieve a desired confidence level of accuracy for the nonparametric regression estimators. Since sequential procedures allow an experimenter to make decisions based on the smallest number of observations without compromising accuracy, application of sequential procedures to a nonparametric regression model at a given point or series of points is considered. The motivation for using such procedures is: in many applications the quality of estimating an underlying regression function in a controlled experiment is paramount; thus, it is reasonable to invoke a sequential procedure of estimation that chooses a sample size based on recorded observations that guarantees a preassigned accuracy.We have employed sequential techniques to develop a procedure for constructing a fixed-width confidence interval for the predicted value at a specific point of the independent variable. These fixed-width confidence intervals are developed using asymptotic properties of both Nadaraya-Watson and local linear kernel estimators of nonparametric kernel regression with data-driven bandwidths and studied for both fixed and random design contexts. The sample sizes for a preset confidence coefficient are optimized using sequential procedures, namely two-stage procedure, modified two-stage procedure and purely sequential procedure. The proposed methodology is first tested by employing a large-scale simulation study. The performance of each kernel estimation method is assessed by comparing their coverage accuracy with corresponding preset confidence coefficients, proximity of computed sample sizes match up to optimal sample sizes and contrasting the estimated values obtained from the two nonparametric methods with act ual values at given series of design points of interest.We also employed the symmetric bootstrap method which is considered as an alternative method of estimating properties of unknown distributions. Resampling is done from a suitably estimated residual distribution and utilizes the percentiles of the approximate distribution to construct confidence intervals for the curve at a set of given design points. A methodology is developed for determining whether it is advantageous to use the symmetric bootstrap method to reduce the extent of oversampling that is normally known to plague Stein's two-stage sequential procedure. The procedure developed is validated using an extensive simulation study and we also explore the asymptotic properties of the relevant estimators.Finally, application of our proposed sequential nonparametric kernel regression methods are made to some problems in software reliability and finance
The effectiveness of SMS Reminders and the impact of patient characteristics on missed appointments in a public dental outpatient clinic
No full textThis paper reports on the Failure To Attend (FTA) rate of appointments as well as patients following the implementation of SMS reminders in a public dental outpatient service. Given the ineffectiveness of the intervention and a highly representative patient’s profile, this paper identifies the demographic characteristics of patients who miss all of their appointments. Data on appointment attendance, patient demographics and dental service type was collected over a time period of 46 consecutive months. Using descriptive and inferential statistics (chi-square, two sample tests and Marascuilo procedure) we found the SMS intervention was ineffective in reducing the FTA rates. Further, patients associated with high rates of non-attendance exhibited one or more of the following characteristics: male; age 26 – 44; non-concession card holders; a person of Indigenous, local, Asian or African descent, and of refugee status, persons living in low socio-economic areas; and appointments in General Care and Student Clinics. Whilst the literature overwhelmingly attributes SMS reminders to improving the attendance rate of patients in outpatient clinics, our contradictory findings suggest a more targeted approach in settings whose patients exhibit strong characteristics associated with non-attendance
Co-creating Value in Citizen–Government Engagement
No full textThe theoretical foundations of citizen engagement are largely unexplored despite the existence of frameworks for social media-based citizen–government interactions. Although the service-dominant (S-D) logic-informed theorization of the related customer engagement (CE) concept has been widely actuated, knowledge of citizens’ engagement with the government is scant, causing a significant research gap. Therefore, based on the S-D logic-informed CE, we conceptualize citizens–government engagement (CGE) as a citizen’s cognitive, emotional, behavioural, and social activity during/related to citizen–government interactions. Then, we propose an S-D logic-informed CGE framework in the social media context. In this Facebook Australia commissioned research, we empirically test the framework using a case study of 68 Australian federal government-owned Facebook pages in a sample from January 2013 to January 2016. Our results demonstrated that CGE is multidimensional and fluctuating, like CE. We also demonstrate the role of CGE in value co-creation, as perceived by citizens, and identify four CGE-based activation outcome practices: broadcasting, connecting, communicating, and activating. Finally, important implications for research and practice are presented, along with a discussion of the study’s limitations and avenues for future research
Univariate and multivariate process yield indices based on location-scale family of distributions
No full textSeveral measures of process yield, defined on univariate and multivariate normal process characteristics, have been introduced and studied by several authors. These measures supplement several well-known Process Capacity Indices (PCI) used widely in assessing the quality of products before being released into the marketplace. In this paper, we generalise these yield indices to the location-scale family of distributions which includes the normal distribution as one of its member. One of the key contributions of this paper is to demonstrate that under appropriate conditions, these indices converge in distribution to a normal distribution. Several numerical examples will be used to illustrate our procedures and show how they can be applied to perform statistical inferences on process capability
A new process capability index for multiple quality characteristics based on principal components
No full textA new process capability index for multiple quality characteristics based on principal component
Co-creating Value in Citizen-Government Engagement
No full textThe theoretical foundations of citizen engagement are largely unexplored despite the existence of frameworks for social media-based citizen–government interactions. Although the service-dominant (SD) logic-informed theorization of the related customer engagement (CE) concept has been widely actuated, knowledge of citizens’ engagement with the government is scant, causing a significant research gap. Therefore, based on the S-D logic-informed CE, we conceptualize citizens–government engagement (CGE) as a citizen’s cognitive, emotional, behavioural, and social activity during/related to citizen–government interactions. Then, we propose an S-D logic-informed CGE framework in the social media context. In this Facebook Australia commissioned research, we empirically test the framework using a case study of 68 Australian federal government-owned Facebook pages in a sample from January 2013 to January 2016. Our results demonstrated that CGE is multidimensional and fluctuating, like CE. We also demonstrate the role o
A complementary application of univariate process capability indices
No full textA complementary application of univariate process capability indice
Estimating capability index in multivariate processes using bootstrap sequential sampling procedures
No full textCapability indices in both univariate and multivariate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a measure of the ratio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared
Software reliability growth models based on local polynomial modeling with kernel smoothing
No full textSoftware reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.<br
Co-creating Value in Citizen–Government Engagement
No full textCo-creating Value in Citizen–Government Engagemen
