GIS and development control system for a local authority in Malaysia

The process of development control involves a technique for the systematic compilation of expert quantitative analysis and qualitative assessment of project land use and property development viability, including its effect on the surrounding area, and the presentation of results in a way that enables the importance of the predicted results, and the scope of modifying or mitigating them to be properly evaluated by the relevant decision making body before a planning permission is rendered. Taking the local authorities in Malaysia as an example, this paper will discuss and demonstrate the development of a GIS database and its integration and application for development and building control. The first part of the paper examines the functions of local authorities particularly in the context of development control. In this study, the procedure involved in granting a planning permission as well as enforcement becomes the main focus because it determines the bulk of the system design. The study identifies seven sub-systems namely planning permission, building control, planning enforcement, geospatial database, information kiosk, document processing and presentation sub-systems. The system is developed using a combination of office automation, CAD, GIS, multimedia and other software packages. This paper will focus on the development of the GIS for the purpose of development control. The components of the GIS database include land information, buildings, existing developments, planning information, development plans, utilities, community facilities, transportation, environment and the socio-economy database. The GIS database will be used at every stage of development control, for example, in initial discussions, registration, invitation of objections, the development control process, the consideration by Technical Committee and the consideration by Planning Committee. It is anticipated that the GIS database could be used by many parties involved in the process as a reference point in evaluating a planning submission. Having access to the database will provide flexibility in assessing a proposed development and deciding on the overall urban growth management programme in the most cost-effective manner. The system will also provide transparency and consistency in the development control procedure.     

A protective estimator for longitudinal binary data subject to non-ignorable non-monotone missingness

Summary.  In longitudinal studies missing data are the rule not the exception. We consider the analysis of longitudinal binary data with non-monotone missingness that is thought to be non-ignorable. In this setting a full likelihood approach is complicated algebraically and can be computationally prohibitive when there are many measurement occasions. We propose a 'protective' estimator that assumes that the probability that a response is missing at any occasion depends, in a completely unspecified way, on the value of that variable alone. Relying on this 'protectiveness' assumption, we describe a pseudolikelihood estimator of the regression parameters under non-ignorable missingness, without having to model the missing data mechanism directly. The method proposed is applied to CD4 cell count data from two longitudinal clinical trials of patients infected with the human immunodeficiency virus.     

CEO Incentives and Customer‐Supplier Relations

This study explores how suppliers adjust their relation‐specific investments (RSI) in response to the different risk‐taking incentives provided by the customer firm to its CEO, during normal and transition periods. We investigate this relation using 17,553 customer–supplier transactions over the 1993–2013 period. We find strong evidence consistent with the risk‐taking argument. Specifically, we find that an increase in the risk‐taking incentives of customer CEOs leads to a decline in suppliers’ RSI in normal periods, but an increase in RSI during transition periods. We employ the FAS‐123R mandate to show that an exogenous reduction in customer CEO's incentive pay increases suppliers’ RSI. We reaffirm the effect with the passage of the Sarbanes–Oxley Act as a secondary quasi‐natural experiment. Finally, we examine several scenarios that either amplify or attenuate the observed relation, based on factors such as financial constraints, distress, growth opportunities, industry competition, and other firm characteristics. Our study contributes to the literature that examines the interplay between corporate policy and product market relationships.     

Nonparametric versus Parametric Estimation of the Cure Fraction Using Interval Censored Data

This article discusses estimation of the cure rate by means of the bounded cumulative hazard (BCH) model using interval censored data. The parametric and nonparametric estimation methods within the framework of the EM algorithm were employed for cure rate estimation and their results compared. The Turnbull estimator was used in the nonparametric estimation while in parametric method both the exponential and Weibull distributions were considered. We show via simulation that the nonparametric method is a viable alternative to the parametric one when the censoring rate is rapidly increasing.     

The Large Sample Distribution of the Weighted Log Rank Statistic Under General Local Alternatives

We derive the large sample distribution of the weighted log rank statistic under a general class of local alternatives in which both the cure rates and the conditional distribution of time to failure among those who fail are assumed to vary in the two treatment arms. The analytic result presented here is important to data analysts who are designing clinical trials for diseases such as non-Hodgkins lymphoma, leukemia and melanoma, where a significant proportion of patients are cured. We present a numerical illustration comparing powers obtained from the analytic result to those obtained from simulations. This revised version was published online in July 2006 with corrections to the Cover Date.     

Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models

This article considers the utility of the bounded cumulative hazard model in cure rate estimation, which is an appealing alternative to the widely used two-component mixture model. This approach has the following distinct advantages: (1) It allows for a natural way to extend the proportional hazards regression model, leading to a wide class of extended hazard regression models. (2) In some settings the model can be interpreted in terms of biologically meaningful parameters. (3) The model structure is particularly suitable for semiparametric and Bayesian methods of statistical inference. Notwithstanding the fact that the model has been around for less than a decade, a large body of theoretical results and applications has been reported to date. This review article is intended to give a big picture of these modeling techniques and associated statistical problems. These issues are discussed in the context of survival data in cancer.     

Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching

Motivated from a colorectal cancer study, we propose a class of frailty semi-competing risks survival models to account for the dependence between disease progression time, survival time, and treatment switching. Properties of the proposed models are examined and an efficient Gibbs sampling algorithm using the collapsed Gibbs technique is developed. A Bayesian procedure for assessing the treatment effect is also proposed. The deviance information criterion (DIC) with an appropriate deviance function and Logarithm of the pseudomarginal likelihood (LPML) are constructed for model comparison. A simulation study is conducted to examine the empirical performance of DIC and LPML and as well as the posterior estimates. The proposed method is further applied to analyze data from a colorectal cancer study.     

Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data

Summary.  In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to women who are infected with the human immunodeficiency virus, instead of a single outcome variable, there are multiple binary outcomes (e.g. abnormal heart rate, abnormal blood pressure and abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated by using generalized estimating equations (GEEs), and consistent estimates can be obtained under the assumption of a missingness completely at random mechanism. When the missing data mechanism is missingness at random, i.e. the probability of missing a particular outcome at a time point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models by using a single modified GEE based on an EM-type algorithm. The method proposed is motivated by the longitudinal study of cardiac abnormalities in children who were born to women infected with the human immunodeficiency virus, and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that, under a missingness at random mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided that the correlation model has been correctly specified, whereas estimates from standard GEEs can lead to substantial bias.     

Estimation of the shape and scale parameters of Pareto distribution using ranked set sampling

In the current paper, the estimation of the shape and location parameters α and c, respectively, of the Pareto distribution will be considered in cases when c is known and when both are unknown. Simple random sampling (SRS) and ranked set sampling (RSS) will be used, and several traditional and ad hoc estimators will be considered. In addition, the estimators of α, when c is known using an RSS version based on the order statistic that maximizes the Fisher information for a fixed set size, will be considered. These estimators will be compared in terms of their biases and mean square errors. The estimators based on RSS can be real competitors against those based on SRS.