Optimal dynamic treatment regimes :regret-regression method with myopic strategies

PhD ThesisOptimal dynamic treatment strategies provide a set of decision rules that are based on a patient’s history. We assume there are a sequence of decision times j = 1,2,...,K. At each time a measurement of the state of the patient Sj is obtained and then some action Aj is decided. The aim is to provide rules for action choice so as to maximise some final value Y . In this thesis we will focus on the regret-regression method described by Henderson et al. (2009), and the regret approach to optimal dynamic treatment regimes proposed by Murphy (2003). The regret-regression method combines the regret function with regression modelling and it is suitable for both long term and myopic (short-term) strategies. We begin by describing and demonstrating the current theory using the Murphy and Robins G-estimation techniques. Comparison between the regret-regression method and these two methods is possible and it is found that the regret-regression method provides a better estimation method than Murphy’s and Robins G-estimation. The next approach is to investigate misspecification of the Murphy and regret-regression models. We consider the effect of misspecifying the model that is assumed for the actions, which is required for the Murphy method, and of the model for states, which is required for the regret-regression approach. We also consider robustness of the fitting algorithms to starting values of the parameters. Diagnostic tests are available for model adequacy. An application to anticoagulant data is presented in detail. Myopic one and twostep ahead strategies are studied. Further investigation involves the use of Generalised Estimating Equations (GEEs) and Quadratic Inference Functions (QIF) for estimation. We also assess the robustness of both methods. Finally we consider the influence of individual observations on the parameter estimates.University of Malaya, Malaysia: The Ministry of Higher Education, Malaysia

Anticipated BSDEs driven by fractional Brownian motion with time-delayed generator

This paper discusses a new type of anticipated backward stochastic differential equation with a time-delayed generator (DABSDEs, for short) driven by fractional Brownian motion, also known as fractional BSDEs, with Hurst parameter $H\in(1/2,1)$, which extends the results of the anticipated backward stochastic differential equation to the case of the drive is fractional Brownian motion instead of a standard Brownian motion and in which the generator considers not only the present and future times but also the past time. By using the fixed point theorem, we will demonstrate the existence and uniqueness of the solutions to these equations. Moreover, we shall establish a comparison theorem for the solutions

Backward Stochastic Differential Equations (BSDEs) Using Infinite-dimensional Martingales with Subdifferential Operator

In this paper, we focus on a family of backward stochastic differential equations (BSDEs) with sub-differential operators that are driven by infinite-dimensional martingales which involve symmetry, that is, the process involves a positive definite nuclear operator Q. We shall show that the solution to such infinite-dimensional BSDEs exists and is unique. The existence of the solution is established using Yosida approximations, and the uniqueness is proved using Fixed Point Theorem. Furthermore, as an application of the main result, we shall show that the backward stochastic partial differential equation driven by infinite-dimensional martingales with a continuous linear operator has a unique solution under the condition that the function F equals to zero

Optimised reduction of surgical gloves pinholes using forward search method

This research investigates the factors that affect the existence of pinholes in surgical gloves during the manufacturing process. Since eight factors affect the existence of pinholes in surgical gloves, a two-level fractional factorial design 28-4 was used to study the main effects and the first-order interactions of the multiple variables. Multiple linear regressions are used to model the data. This paper also examines the presence of influential points in the data using the influential measures in linear regression such as Cook’s Distance, DFFITS, DFBETAS, Studentized Residual, Standardized Residual, Hadi’s measure, and the robust forward search. The impact of influential points is further assessed through deletion of potential influential points and model selection using adjusted R2, information criterion, and stepwise selection to see whether these influential points significantly improved the existing model

Outlier detection in 2 × 2 crossover design using Bayesian framework

We consider the problem of outlier detection method in 2×2 crossover design via Bayesian framework. We study the problem of outlier detection in bivariate data fitted using generalized linear model in Bayesian framework used by Nawama. We adapt their work into a 2×2 crossover design. In Bayesian framework, we assume that the random subject effect and the errors to be generated from normal distributions. However, the outlying subjects come from normal distribution with different variance. Due to the complexity of the resulting joint posterior distribution, we obtain the information on the posterior distribution from samples by using Markov Chain Monte Carlo sampling. We use two real data sets to illustrate the implementation of the method

Face recognition attendance system using Local Binary Pattern (LBP)

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate

Bioteknologi moden: aplikasi, status, isu etika dan perspektif penyelidik dan industri terhadap prinsip etika utama

Perkembangan bioteknologi moden di Malaysia menimbulkan pelbagai kontroversi. Terkini, Lembaga Biokeselamatan Kebangsaan telah meluluskan 29 jenis produk kejuruteraan genetik (GE) ke dalam pasaran Malaysia. Antara isu-isu etika dalam bioteknologi moden dan produknya ialah kesan jangka masa panjang, kesan terhadap kesihatan dan alam sekitar, kesan sosio-ekonomi dan isu ‘bertindak seolah-olah Tuhan’ Malaysia seharusnya bergerak seiring dengan negara-negara barat untuk mewujudkan garis panduan etika bioteknologi moden. Garis panduan etika memerlukan prinsip panduan. Objektif artikel ini ialah untuk membincangkan aplikasi, status, isu-isu etika bioteknologi moden dan perspektif penyelidik dan industri di Malaysia terhadap prinsip etika sekular utama iaitu autonomi, kebajikan, tidak memudaratkan dan keadilan. Penyelidikan ini dijalankan secara perbincangan kumpulan fokus (FGD) menggunakan instrumen semi berstruktur. Perbincangan yang diadakan mengambil masa sekitar 3 jam dan telah dirakam. Rakaman perbincangan telah ditranskrip secara ‘verbatim’ sebelum dianalisis. Hasil kajian menunjukkan bahawa semua panel amat bersetuju mengenai kepentingan dan kesesuaian keempat-empat prinsip etika untuk diguna pakai sebagai prinsip bagi garis panduan etika bioteknologi moden di Malaysia. Para panel telah mencadangkan penyusunan semula empat prinsip etika kepada tiga dan telah menambahbaik terma, menghasilkan prinsip autonomi dan kepentingan awam; kebajikan dan tidak memudaratkan; dan keadilan dan bukan diskriminasi. Panel juga telah menambahbaik huraian bagi definisi setiap prinsip yang ada

Mean-Field and Anticipated BSDEs with Time-Delayed Generator

In this paper, we discuss a new type of mean-field anticipated backward stochastic differential equation with a time-delayed generator (MF-DABSDEs) which extends the results of the anticipated backward stochastic differential equation to the case of mean-field limits, and in which the generator considers not only the present and future times but also the past time. By using the fixed point theorem, we shall demonstrate the existence and uniqueness of the solutions to these equations. Finally, we shall establish a comparison theorem for the solutions

Anticipated BSDEs Driven by Fractional Brownian Motion with a Time-Delayed Generator

This article describes a new form of an anticipated backward stochastic differential equation (BSDE) with a time-delayed generator driven by fractional Brownian motion, further known as fractional BSDE, with a Hurst parameter H∈(1/2,1). This study expands upon the findings of the anticipated BSDE by considering the scenario when the driver is fractional Brownian motion rather instead of standard Brownian motion. Additionally, the generator incorporates not only the present and future but also the past. We will demonstrate the existence and uniqueness of the solutions to these equations by employing the fixed point theorem. Furthermore, an equivalent comparison theorem is derived

Backward Stochastic Differential Equations (BSDEs) Using Infinite-Dimensional Martingales with Subdifferential Operator

In this paper, we focus on a family of backward stochastic differential equations (BSDEs) with subdifferential operators that are driven by infinite-dimensional martingales. We shall show that the solution to such infinite-dimensional BSDEs exists and is unique. The existence and uniqueness of the solution are established using Yosida approximations. Furthermore, as an application of the main result, we shall show that the backward stochastic partial differential equation driven by infinite-dimensional martingales with a continuous linear operator has a unique solution under the special condition that the Ft-progressively measurable generator F of the model we proposed in this paper equals zero