GSAS: Dissertations

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Factors affecting stroke mortality in Thailand
Pimchanok Puthkhao; Duanpen Theerawanviwat (National Institute of Development Administration, 2020)
This dissertation aimed to compare the socioeconomic and health-related characteristics of stroke and non-stroke deaths and to determine the factors affecting stroke mortality, with non-stroke death considered as a competing risk. Secondary data with a 10-12 years follow-up period from the Thai Epidemiologic Stroke (TES) Study were used. The Thai Epidemiologic Stroke (TES) Study is a prospective community-based cohort study that recruited participants from the general population from five Thai regions. Between 2004 and 2006, 19,620 participants aged 45-80 years, free of stroke, participated in the baseline survey. The participants were followed up for mortality from the survey date until the date of death or the end of follow-up of December 31, 2016, whichever came firsts. During a median follow-up time of 11.08 years (202,803 person-years at risk), 305 participants died of a stroke (1.55% of total participants and accounted for 8.76% of total deaths), and 3,176 participants died of non-stroke cause (16.19% of total participants and 91.24% of total deaths). Stroke mortality was 150.39/100,000 (95% confidence interval [95% CI], 134.43-168.25/100,000) personyears, and the non-stroke mortality was 1,566.05/100,000 (95% CI, 1,512.52- 1,621.47/100,000) person-years. Multivariate cause-specific Cox regression and Fine-Gray competing risk regression analyses were used to identify the factors affecting stroke mortality, with non-stroke mortality considered as a competing event. Cause-specific hazard ratios (HR) and the Subdistribution hazard ratio (SHR) with their 95% confidence intervals (CI) were used to illustrate the associations.
Ontology-based knowledge discovery and exploring technology influencers from patent data
Pranomkorn Ampornphan; Sutep Tongngam (National Institute of Development Administration, 2020)
A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to the patent analysis, for example, to technology overview, investment volume, and the technology life cycle. There are also data analytic methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making as well as a taxonomy of concepts for knowledge representation by developing an ontology-based semantic search. The main contributions of this study were to extract knowledge from a patent relational database into two approaches; 1) Develop an ontology-based from patent data to provide an effective search for technological concepts, and 2) Explore technology influencers from patents using data analytics. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. In the first approach, the patent data was defined as terms, concepts, classes, and properties to create a patent ontology. A patent ontology consisted of the relations of each concept that were represented as an ontology map. Next, a patent database was created to integrate with the ontology map to develop an ontology-based application. The result from this stage was an ontology-based that facilitates as a recommender system. The second approach related to exploring technology influencers from patent data. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data being analyzed by which the association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. This study demonstrated 2 approaches for knowledge discovery from patent data by which; 1) the expected output from the ontology-based will be used to support information searching for more relevant and precise information, and 2) the resultsfrom data analytics showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.
Ant colony system with Thailand green travelling problem
Punyapas Chawaratthanarungsri; Sutep Tongngam (National Institute of Development Administration, 2020)
Most industries focus on how to get the benefits from processing and transmitting even in tourism industry. Technology has been used to meet the need of travelers in order to access more information such as flight, route, hotel, transportation and others by themselves. There are some techniques of computer science that solve about travelling problem such as Artificial Intelligence and Animal stimulation is used such as ant behavior etc. Thus, this research proposes how to apply Ant Colony Optimization with travelling problem. As the result, Brute force was taken into consideration to compare the capabilities of The Ant Colony System. The results obtained from Ant Colony System have some routes equal to the shortest distance of Brutes Force, but some Brute Force routes have shortest distances. When looking at the performance of algorithm, the processing time to generate all possible paths of the Brute Force takes more time than Ant Colony System. The efficiency of the Brute Force algorithm is O(N2)(Christian and Thierry) while Any Colony System Only O(m logm)(Walter,). Using Ant Colony System by adding other conditions such as changing vehicles at each tourist attraction to complete the planning. It can be further expanded into a system of advice tourist for tourist recommended plan.
Relationships between psychological factors, innovative performance, marketing capability, and entrepreneurial success among Thai fruit and vegetable processing and preservation SMEs in Thailand
Natthawat Wiwatkitbhuwadol; Arnond Sakworawich (National Institute of Development Administration, 2019)
An entrepreneur starts and runs a business through the pursuit of opportunities with the determination to use his/her knowledge, abilities, and experiences to effectively run his/her organization and with the resources at hand. He/she is a creative person who finds new approaches to market existing merchandise or better ways to improve and develop existing production processes to maximize the organization’s benefits. He/she is willing to undertake a business venture in exchange for profits and satisfaction. These are the characteristics of a potentially successful entrepreneur. Entrepreneurial success is the primary goal of every entrepreneur, in the pursuit of which he/she must endure different kinds of problems to achieve this goal, and there are many ways to measure business success. The aim of this study is to analyze the relationships between the psychological factors, innovative performance, marketing capability, and entrepreneurial success among Thai Fruits and Vegetables Processing and Preservation SMEs in Thailand. This is one of the first empirical studies to adopt the Giessen-Amsterdam Model of Entrepreneurial Success as the main research model with some added variables that may affect entrepreneurial success identified from a literature review. Another research interest is the impact of the rising number of Thai Fruit and Vegetable Processing and Preservation SMEs entrepreneurs on global businesses due to increased quantities of imitation goods and services. The results of the study show that innovative performance, and marketing capability are highly related to entrepreneurial success. The developed strategies using innovative performance, and marketing capability drivers could help Thailand’s SMEs entrepreneurs to be successful in a variety of industries.
Closed-loop supply chain model analysis
Jariya Seksan; Kannapha Amaruchkul (National Institute of Development Administration, 2019)
In this dissertation, we analyzed the closed-loop supply chain with buyback contract. We considered the model with one manufacturer and one retailer. First, we derived the optimal order quantity, the optimal contract parameter and consider the condition that buyback contract can coordinate supply chain. Next, we extended the first part by considering for the return policy. We considered when customers return follows the customer’s willingness to return function which depends on return price. We derived the optimal return price and the optimal order quantity follows that price. We found that the optimal return price depends on the buyback price offered by the manufacturer. Then, we considered when the retailer engages in secondary market. The retailer sells the returned product at discount price of the new product in the secondary market. We derived for the optimal resale unit and secondary product price. We found that the retailer can gain more profit when selling returned product in secondary market. In addition, we found that buyback contract can coordinate closedloop supply chain. Using numerical results, some of the relationships between parameters and the expected profits were shown. The results showed that when the wholesale price increases, the order quantity and retailer’s expected profit decrease, but the manufacturer’s expected profit increases. Besides, when the buyback price increases, the order quantity and the retailer’s expected profit increase, but manufacturer’s expected profit decreases. The results also show that the return price 𝑟 and the buyback contract parameters (𝑤, 𝑏) affect the performances of closed-loop supply chain and both manufacturer and retailer receive more profit when the retailer sets the optimal return price. Finally, we present the special topic of using buyback contract in closed-loop supply chain to achieve sustainability: case of electrical and electronic equipment (EEE) and show that supply chain sustainability can be achieved through a buyback contract.
Two-sample multivariate tests for high-dimensional data with one unknown covariance matrix
Nittaya Thonghnunui; Samruam Chongcharoen (National Institute of Development Administration, 2022)
Test statistics for one-sided and two-sided multivariate hypotheses for the high-dimensional two-sample problem with one unknown covariance matrix are proposed in this dissertation. The hypothesis tests considered are H0 : μ1 = μ2 versus H1 : μ1 > μ2 and H0 : μ1 = μ2 versus H1 : μ1 < μ2 for the one-sided case and H0 : μ1 = μ2 versus H1 : μ1 ≠ μ2 for the two-sided case. The tests are developed based on the idea of keeping as much information from the pooled sample covariance matrix as possible by arranging the blocks along its diagonal. The asymptotic distributions of the test statistics are derived under the null hypothesis. The performances of the proposed tests were evaluated with both equal and unequal sample sizes via a simulation study. The simulation results show that the proposed tests performed well for both equal and unequal sample sizes. An illustration of the efficacies of the proposed tests was carried out on a genetic microarray dataset.
A provably group authentication protocol for various LTE networks
Boriphat Kijjabuncha; Pipat Hiranvanichakorn (National Institute of Development Administration, 2018)
Group authentication is beneficial for group work in the Long Term Evolution (LTE) networks because it reduces the traffic of networks. For practical use, members of a group should be able to come from different network providers. In addition, while some group members use a network service, others may use other network services. Although the group members are on different networks, they should be able to work together. To fulfill these needs, we propose a secure group authentication protocol (SEGA) in which each group member uses his/her long-term private key and public key to create shared secret (keys) with network devices, such as Home and mobile management entity (MME). These shared keys are computed by using the DiffieHellman key exchange and are utilized in the authentication process. By using this technique instead of pre-shared keys between mobile devices and network devices, SEGA is flexible and scalable. In SE-GA, only the first member in an MME’s area has to authenticate himself/herself with the Home, while the remaining members in the area can authenticate directly with the MME. Thus the protocol reduces the amount of network usage. In this research, authentication proof is also given using the well-known BAN logic. Security analysis of the proposed protocol is also given and a comparison of our protocol with SE-AKA and GLARM was demonstrated. According to the comparison, we can see that the proposed protocol outperforms the former ones.
Multi-objective genetic algorithm for supervised clustering
Vipa Thananant; Surapong Auwatanamongkol (National Institute of Development Administration, 2018)
Supervised clustering organizes data instances into clusters on the basis of similarities between the data instances as well as class labels for the data instances. Supervised clustering seeks to meet multiple objectives, such as compactness of clusters, homogeneity of data in clusters with respect to their class labels, and separateness of clusters. With these objectives in mind, a new supervised clustering algorithm based on a multi-objective crowding genetic algorithm, named SC-MOGA, is proposed in this thesis. The algorithm searches for the optimal clustering solution that simultaneously achieves the three objectives mentioned above. The SC-MOGA performs very well on a small dataset, but for a large dataset it may not be able to converge to an optimal solution or can take a very long running time to converge to a solution. Hence, a data sampling method based on the Bisecting K-Means algorithm is also introduced, to find representatives for supervised clustering. This method groups the data instances of a dataset into small clusters, each containing data instances with the same class label. Data representatives are then randomly selected from each cluster. The experimental results show that SC-MOGA with the proposed data sampling method is very effective. It outperforms three previously proposed supervised clustering algorithms, namely SRIDHCR, LK-Means and SCEC, in terms of four cluster validity indexes. The experimental results show that the proposed data sampling method not only helps to reduce the number of data instances to be clustered by the SC-MOGA, but also enhances the quality of the data clustering results. Moreover, the biased initial approach is proposed in this thesis to find a good initial population to bias. The experimental results show that biased initial population of SCMOGA will improve clustering quality and the more percentage of biased initial population the better clustering quality.
Human emotion recognition in Thai short text
Jirawan Charoensuk; Ohm Sornil (National Institute of Development Administration, 2018)
Emotion classification is one of the topics in effective computing applicable in various research areas such as speech synthesis, image processing, and especially, text processing. Emotion classification is aimed at identifying a suitable emotion label for each review. In this research, a hierarchical classification framework to identify emotions (objective opinion and anger, disgust, fear, sadness, happiness, and surprise) is proposed for actual customer reviews written in Thai. The hierarchical classification framework consists of three levels: opinion, sentiment, and emotion. First, the opinion level distinguishes customers’ reviews into two types, namely objective and subjective opinions. Second, the sentiment level is used to categorize the subjective opinions as either positive or negative. Last, in the emotion level, an emotion label is assigned to an opinion as either anger, disgust, fear, happiness, sadness, or surprise. The proposed method consists of three main processes: (1) text preprocessing, (2) feature extraction, and (3) emotion classification. Text preprocessing provides necessary information and normalization of words in the reviews and comprises word segmentation, part-ofspeech (POS) tagging, word replacement, and stop-word elimination. Feature extraction is a process to construct a vector space model (VSM) for opinion classification. Five feature sets for generating the VSM are created by using a corpusand lexicon-based approach: the term frequency-inverse document frequency (Tf-Idf) of unigram words (TUW), bigram words (TBW), unigram POS (TUP), and bigram POS (TBP), and a Thai sentiment lexicon (TSL). Furthermore, a decision tree, multinomial naïve Bayes, and a support vector machine (SVM) are used as classifiers in the emotion classification process. The experimental results show that for the hierarchical approach where the subjectivity of a review is first determined, the polarity of an opinion is identified, and then the emotional label is calculated yielded the highest performance with an accuracy of 69.60%. Overall, TBW was the most effective feature subset used for filtering opinions, determining polarity, and classifying negative emotions. Lexicon resources such as TSL and the POS tag sets in the morphology level improved the accuracy of opinion filtering in two- and three-level hierarchical classification. SVM achieved a high performance in identifying contrasting opinions such as objective versus subjective opinions and positive versus negative sentiment. Meanwhile, multinomial naïve Bayes performed the best when identifying closely related emotions such as happiness versus surprise in positive emotion classification.
A penalty function in binary logistic regression
Piyada Phrueksawatnon; Jirawan Jitthavech (National Institute of Development Administration, 2018)
An algorithm is proposed to determine the logistic ridge parameter minimizing the MSE of the estimated parameter estimators, together with a theorem on the upperbound of the optimal logistic ridge parameter to facilitate the nonlinear optimization. A simulation is used to evaluate the relative efficiencies of the proposed estimator and other six well-known ridge estimators with respect to the maximum likelihood estimator. The simulation results confirm that the relative efficiency of the proposed estimator is highest among other well-known estimators. Finally, a real-life data set is used to repeat the evaluation and the conclusion is the same as in the simulation
Analyzing social media content to gain competitive intelligence
Jitrlada Rojratanavijit; Preecha Vichitthamaros (National Institute of Development Administration, 2017)
The emergence of social media in Thailand has given millions of users a platform to express and share their opinions about products and services, among other subjects, and so Twitter is considered to be a rich source of information for companies to understand their customers by extracting and analyzing sentiment from Tweets. The main goal was to investigate the possible uses of Twitter information for businesses in Thailand to take advantage of and to solve any associated limitations caused by the semantics of the Thai language. Hence, social media content, specifically Tweets were utilized to generate Competitive Intelligence (CI). A new method for Twitter sentiment analysis called ASTS was proposed by using both supervised learning and lexiconbased techniques. Experiments were conducted using Tweets on three mobile network operator companies: AIS, DTAC, and TRUEMOVEH obtained using the Twitter search API focused on Tweets in Thai. A total of 72,661 were collected over a period of six months (from October 1, 2014 to March 31, 2015). ASTS consists of three modules: (1) data collection, (2) data pre-processing, and (3) classification and evaluation. A collection program was developed to search for keywords in the Twitter feed using the Twitter Search API and setting the language parameter “lang=th” and excluding reTweets. The process for the data pre-processing module was divided into three steps: (1) Text extraction from the Tweets, (2) Text preprocessing, and (3) Thai word segmentation. For the classification and evaluation module, the main intention was to identify opinion polarity, positive, negative, and neutral. The classification process was divided into two sub-modules: opinion filtering using supervised learning techniques and opinion polarity identification using lexiconbased techniques. Experimental results showed that the proposed method overcomes previous limitations from other studies and was very effective in most cases. The average accuracy is 84.80% with 82.42% precision, 83.88% recall and 82.97% Fmeasure. In particular, this clearly shows that opinion filtering helped to analyze Tweets more accurately. A case study approach for CI in social media aptly demonstrated the use of ASTS. Out of a total of 20,269 Tweets, 9,631 mentioned AIS (47.52%), 7,099 mentioned DTAC (35.02%), and 3,539 mentioned TRUEMOVEH (17.46%). The sentiment scores from the analysis results of using ASTS showed the overall customer sentiment for the companies. The sentiment score for TRUEMOVEH (-0.27) was slightly better than AIS (-0.38) and DTAC (-0.45). Benchmarking against competitors is essential information for CI. Strength and weakness analysis on the companies was derived using radar charts of the benchmarking of sentiment scores on the top five keyword mentions (net, wifi, promotion, switching, and employee). Furthermore, examples of using CI in terms of monitoring, opportunity events, and early warning alerts were presented. Opportunity events can be advantageous in response to negative sentiment Tweets on competing companies and can help a company to entice customers away from competing companies. Early warning alerts are based on negative sentiment Tweets on a company from which it should quickly identify customer dissatisfaction and then correct the associated problem. The results of this study show the usefulness of the proposed method for theoretical reference and as a practical guide. The findings from this analysis prove that CI extracted from social media content can help businesses to understand their customers’ opinions and compare them with those of their competitors. As a result, this research illustrates that CI from analyzing social media content has great potential to produce useful information, actionable knowledge, and critical insights for companies to enhance competitiveness and solve business problems.
The upper bounds of the ruin probability for an insurance discrete-time risk model with proportional reinsurance and investment
Apichart Luesamai; Samruam Chongcharoen (National Institute of Development Administration, 2018)
In this study, the two upper bounds of the ruin probability for discrete time risk model derived by adding two controlled factors to the classical discrete time risk model: proportional reinsurance and investment are proposed. These upper bounds are derived using an inductive method and rely on a recursive form of the finite time and/or an integral equation of ultimate (infinite time) of ruin probability which is also derived in this study. Both of the upper bounds are formulated by the assumption that the retention level of reinsurance and the amount of stock investment during each time period are controlled as constant values. The first upper bound can be used with the finite time ruin probability and the ultimate ruin probability under the condition that the value of the adjustment coefficient can be found. The second upper bound is formulated by a using new worse than used distribution. This upper bound can only be used with the finite time ruin probability, and its value can be found even though the value of the adjustment coefficient does not exist. However, this upper bound has limitations on the total claims amount which the total claims amount in each time period must come from the summation of independent and identically distributed (i.i.d.) claim amounts, and the number of claims is also i.i.d. in each time period.
Presentation of risk warning statement moderate the relationships between risk-taking traits, risk preference, and financial risk-taking behavior
Phatid Rongsirikul; Arnond Sakworawich (National Institute of Development Administration, 2021)
This research aimed to study the relationship between people’s risk-taking traits, risk preference, and financial risk-taking behavior in the situation where the risk warning statements existed. The study was based on an online experiment conducted in Thailand. The results were taken from 640 participants joining and contributing their answers to the tests. In the context of experimental design, each participant was randomly assigned to different groups in order to investigate the effect of risk warning statements on the relationships. The results suggested that there existed a relationship between risk-taking traits, risk preference, and financial risk-taking behavior such that high risk-taking traits were linked to a low degree of risk aversion and then caused high financial risk-taking behavior of the people. Given the presentation of the risk warning statements, there was no significant effect found on reducing the financial risk-taking behavior; however, an effect was found on risk preference for a strong version of the risk warning statement. In terms of measurements, additionally, the research also proposed an alternative measure of people’s risk preference based on the so-called Dollar Equivalence (DE) which was a tweaked concept of Probability Equivalence (PE). Regarding the results, the DE was proven to be a superior measure of risk preference compared with the PE.
Feature selection using genetic algorithm
Kanyanut Homsapaya; Ohm Sornil (National Institute of Development Administration, 2017)
In this dissertation, a method of feature selection in machine learning, and more particularly supervised learning is presented. Supervised learning is a machine learning task that infers answers from a training data set. In machine learning, training datasets are employed in order to create a model which enables reasonable predictions, while in supervised learning, each training example is a training set consisting of instances and labels, and the learning objective is to be able to predict the label of a new unseen instance with as few errors as possible. In recent years, many proposed learning algorithms that perform fairly well have been proposed. The factors to accomplish successful model building depend on many aspects such as noise and size of data. Most often for learning algorithms, it is assumed that training data is represented by a vector of numerical data for which each measurement is a feature, and an important question related to machine learning is how to represent instances using vectors of these to yield high learning performance.
Efficacy of pixel swap-based steganographic algorithms in grayscale images
Saitulaa Naranong; Surapong Auwatanamongkol (National Institute of Development Administration, 2021)
The field of steganography deals with the encoding of hidden messages into other data, called the cover, in a manner that makes it non-obvious that a hidden message exists. Though steganography does not necessarily, on its own, encrypt the hidden data beyond deciphering, it can be used as a supplement to encryption, avoiding unnecessary attention from adversaries who may otherwise take additional measures if aware of the secret message. Many types of cover media may be used, ranging from text and formatted text documents, to audio and video, and images. Our study focuses on grayscale images as the choice of cover media. Many kinds of image steganography already exist. The most common, Least Significant Bit (LSB) steganography, efficiently hides data within pixels’ intensity values, in a manner unnoticeable to the naked eye. Because LSB alters the cover image’s first-order statistics, however, it is often detectable through steganalysis methods such as Sample Pair Analysis. While less-detectable variants of LSB, as well as other methods, have been separately introduced, we focus on permutationbased methods that avoid this disadvantage through not alternating the first-order statistics to begin with. Several pixel-swapping algorithms have already been introduced in the literature. We generalize upon those methods by allowing general permutations within larger sets of pixels and intensities, called permissible sets. By design, these permissible sets are an invariant, ensuring that both encoder and decoder read the same ones, even post-permutation. To serve as support for this technique, a supporting theory of multiset permutation is devised and applied. The wider range of possible permutations increases the bit-per-pixel embedding rate over swap-based methods, in a manner that also reduces detectability. Direct implementation and comparison shows our method to improve upon previous swap-based steganography for the Microsoft Research Cambridge dataset of general images, for fixed bit-per-pixel rates. It also shows a larger improvement for the NoisyOffice dataset of scanned images.
A block diagonal covariance matrix test and discriminant analysis of high-dimensional data
Poompong Kaewumpai; Samruam Chongcharoen (National Institute of Development, 2017)
In this dissertation, a new test statistic for testing for a block diagonal covariance matrix structure with a multivariate normal population where the number of variables p exceeds the number of observations n is proposed. Whereas classical approaches such as the likelihood ratio test cannot be applied when p n  , the proposed test statistic is based on the ratio of the estimators of 2 tr and 2 trD , where  is the population covariance matrix and D is the population covariance matrix under the null hypothesis. Furthermore, the asymptotic distribution of the proposed test statistic under the null hypothesis is standard normal. The performance of proposed test statistic was assessed using a simulation study, in which empirical type I error values and the empirical power were used to show its performance. The empirical type I error values were close to the significance level and the empirical power values were closed to 1 in all cases. Moreover, the performance of the proposed test was compared with another previously reported test statistic, and the empirical power values of the proposed test statistic were shown to be higher than those of the comparative test statistic in some cases
Tests for mean vectors in high-dimensional data
Knavoot Jiamwattanapong; Samruam Chongcharoen (National Institute of Development Administration, 2015)
High-dimensional data are ubiquitous and bring new challenges, not only to statisticians, but also to researchers in many scientific fields. They arise in situations where the dimension ( p) , the number of variables in a unit, is larger than the sample size (n), the number of units; data analysis using classical multivariate methods can no longer be applied.
A test statistic for selection of multivariate linear regression models
Srisuda Boonyim; Jirawan Jitthavech (National Institute of Development Administration, 2015)
In this study, a test statistic used to select a multivariate linear regression model based on Mallows’s Cp with the same rationale as the SCp criterion from the system of equations Vichit Lorchirachoonkul and Jirawan Jitthavech (2012: 2386- 2394) proposed. The D statistic, which is the difference between the modified Cp statistics in the reduced model and in the full model, approximates to a standard normal distribution.
Tests for gamma distribution based on its independence property
Bandhita Plubin; Pachitjanut Siripanich (National Institute of Development Administration, 2015)
There are two test statistics proposed in this study in order to test whether data come from a gamma distribution. Both of the proposed test statistics are developed from a modified Kendall coefficient based on the independence property of a gamma distribution. The first one is asymptotically distributed as standard normal and the limit distribution function of the second one was improved using an Edgeworth expansion and the Jackknife method. They are invariant to scale parameters and perform substantially better than existing tests in terms of Type I error rate and test power, especially in cases with samples of moderate size.
Risk-based overbooking model
Murati Soomboon; Kannapha Amaruchkul (National Institute of Development Administration, 2016)
In this study, a two- class revenue management ( RM) model, which combines two of the most important RM strategies for a passenger airline: overbooking and seat inventory control is proposed. Using this model, it is possible to concurrently find both the optimal booking limit and the optimal overbooking limit. Consequently, on a closed- form expression for an optimal booking/ overbooking limit, sensitivity analysis was analytically assessed by changing model parameters such as revenue, the penalty cost associated with unsatisfied demand, the show- up probability, refunds, denied boarding cost, and plane capacity, and a study of its properties and expected profit function carried out.

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