Relevant bibliographies by topics / Market segmentation Mathematical models

Academic literature on the topic 'Market segmentation Mathematical models'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Market segmentation Mathematical models"

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Peng, Chih-Piao, Chiu-Chi Wei, Hsien-Hong Lin, and Su-Hui Chen. "Artificial Intelligence in Market Segment Portfolio for Profit Maximization." Engineering Economics 33, no. 4 (October 26, 2022): 386–97. http://dx.doi.org/10.5755/j01.ee.33.4.29543.

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This paper proposes an approach to select a market segment portfolio to maximize overall profit. The study first uses artificial intelligence algorithms to select the market segments with high profitability. The mathematical programming model is then used to identify the most profitable market segment portfolio. The single-objective programming model is used to find the optimal profit for the baseline condition, and a sensitivity analysis is performed to understand the impact of the variable changes on the results. Then, a multi-objective programming model helps to identify the best profit when the evaluated items reach extreme values. A sensitivity analysis is conducted to reveal the impact of the variable changes on the results. The above results are compared with those of the scoring method. It is found that the artificial intelligence algorithm combined with mathematical programming models can indeed find the market segmentation portfolio with better profits than the conventional methods.
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Raza, Syed Asif. "The impact of differentiation price and demand leakage on a firm’s profitability." Journal of Modelling in Management 10, no. 3 (November 16, 2015): 270–95. http://dx.doi.org/10.1108/jm2-07-2013-0035.

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Purpose – The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability. Design/methodology/approach – Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment. Findings – The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained. Research limitations/implications – In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research. Practical implications – The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price. Social implications – This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response. Originality/value – The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.
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van Hattum, Pascal, and Herbert Hoijtink. "Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models." Journal of Classification 26, no. 3 (December 2009): 297–328. http://dx.doi.org/10.1007/s00357-009-9040-1.

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Savelyeva, Irina, Dmitry Kandaurov, Natalia Pravdina, and Natalya Dzenzelyuk. "WORLD COMMERCIAL SPACE MARKET: POSITIONING OF COUNTRIES AND SEGMENTS OF THE SATELLITE INDUSTRY." Bulletin of the South Ural State University series "Economics and Management" 16, no. 1 (2022): 149–64. http://dx.doi.org/10.14529/em220115.

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The commercialization of space activities is becoming an increasingly significant trend in the development of the global space industry. This trend necessitates the transformation of the Russian space market as well. One of the topical issues of this transformation from an economic point of view is the question of finding the most promising market niches for domestic actors in the space industry. The article is devoted to the analysis of trends in the global space market and the identification of the most promising market segments. The theoretical basis of the study are the general provisions of marketing theory, including the concept of consumer segmentation and the concept of market positioning. The methodological basis of the study includes general scientific methods (analysis, synthesis, deduction, logical approach, comparative analysis), special economic methods of analysis (graphical method), as well as the use of qualitative and quantitative methods of collecting information. The sources of information in the work are the current regulations, data from reputable foreign research organizations, publications of foreign and Russian specialists in the field of space activities. Based on the market segmentation adopted in world practice, trends in the development of the world market in various segments are determined. The fastest growing segments of satellite services are identified, including the segment of remote sensing of the Earth, the broadband segment and the segment of mobile satellite communications, which includes data transmission services for the Internet of things. Based on an estimate of revenue, revenue growth rates and the rate of satellite renewal in orbit, the positions of countries in the respective segments were determined, the leader among which are the United States. The results of the study can become the basis for further study of the mechanisms and business models used by various countries in the field of commercial space, as well as for orienting existing participants in space activities to the most promising market segments.
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Mohammed, Hussam J., Shumoos Al-Fahdawi, Alaa S. Al-Waisy, Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Mazin Abed Mohammed, Seifedine Kadry, and Jungeun Kim. "ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification." Mathematics 10, no. 19 (September 28, 2022): 3530. http://dx.doi.org/10.3390/math10193530.

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Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.
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Khan, Mohammad Farhan, Farnaz Haider, Ahmed Al-Hmouz, and Mohammad Mursaleen. "Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company." Mathematics 9, no. 12 (June 12, 2021): 1369. http://dx.doi.org/10.3390/math9121369.

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Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.
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Andereck, Kathleen L., and Linda L. Caldwell. "Variable Selection in Tourism Market Segmentation Models." Journal of Travel Research 33, no. 2 (October 1994): 40–46. http://dx.doi.org/10.1177/004728759403300207.

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G. Budeva, Desislava, and Michael R. Mullen. "International market segmentation." European Journal of Marketing 48, no. 7/8 (July 8, 2014): 1209–38. http://dx.doi.org/10.1108/ejm-07-2010-0394.

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Purpose – This paper aims to investigate the influence of economic and cultural factors, separately and combined, on international country segments and to reveal the stability of factors and country segments over time. Design/methodology/approach – Principal component analysis is used to develop three economic factors and two cultural factors borrowed from the World Value Survey. Cluster analysis is used to form country clusters based on the economic and cultural factors, separately, and then combined, to detect whether both economics and culture need to be included as bases for macro-country segmentation. Further, the authors look at these issues across time, the beginning of the decade (1990) and then at the end of the decade (1999). Findings – Results support the hypotheses that economics and culture are both necessary for country-level segmentation but reject the hypothesis of cultural convergence as a consequence of technological development and industrialization. The authors confirm that cultural values and beliefs, although persistent, may change gradually under the influence of environmental forces such as economic development. The results support the instability of country segment membership when analyzed over one decade. Economic changes in some countries lead to their movement across segments. Practical implications – Results suggest that managers concerned with international segmentation should include both economic and cultural variables and reevaluate country segment membership continuously rather than relying on results obtained in a single period. Originality/value – Many international segmentation studies have used macro-level, secondary data to identify country clusters based on similarities in political, economic, geographic or cultural variables for a single period. This study extends existing international segmentation models by examining economic and cultural variables (separately, and then combined), and segment membership over time.
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Bassi, Francesca. "Longitudinal models for dynamic segmentation in financial markets." International Journal of Bank Marketing 35, no. 3 (May 15, 2017): 431–46. http://dx.doi.org/10.1108/ijbm-05-2016-0068.

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Purpose Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments. Findings The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market. Originality/value The proposed statistical models are new in the field of financial markets.
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Grover, Rajiv, and V. Srinivasan. "A Simultaneous Approach to Market Segmentation and Market Structuring." Journal of Marketing Research 24, no. 2 (May 1987): 139–53. http://dx.doi.org/10.1177/002224378702400201.

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The authors define a market segment to be a group of consumers homogeneous in terms of the probabilities of choosing the different brands in a product class. Because the vector of choice probabilities is homogeneous within segments and heterogeneous across segments, each segment is characterized by its corresponding group of brands with “large” choice probabilities. The competitive market structure is determined as the possibly overlapping groups of brands corresponding to the different segments. The use of brand choice probabilities as the basis for segmentation leads to market structuring and market segmentation becoming reverse sides of the same analysis. Using panel data, the authors obtain the matrix of cross-classification of brands chosen on two purchase occasions and extract segments by using the maximum likelihood method for estimating latent class models. An application to the instant coffee market indicates that the proposed approach has substantial validity and suggests the presence of submarkets related to product attributes as well as to brand names.
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Dissertations / Theses on the topic "Market segmentation Mathematical models"

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Camilleri, Liberato. "Statistical models for market segmentation." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441119.

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Li, Zhi. "Variational image segmentation, inpainting and denoising." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.

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Variational methods have attracted much attention in the past decade. With rigorous mathematical analysis and computational methods, variational minimization models can handle many practical problems arising in image processing, such as image segmentation and image restoration. We propose a two-stage image segmentation approach for color images, in the first stage, the primal-dual algorithm is applied to efficiently solve the proposed minimization problem for a smoothed image solution without irrelevant and trivial information, then in the second stage, we adopt the hillclimbing procedure to segment the smoothed image. For multiplicative noise removal, we employ a difference of convex algorithm to solve the non-convex AA model. And we also improve the non-local total variation model. More precisely, we add an extra term to impose regularity to the graph formed by the weights between pixels. Thin structures can benefit from this regularization term, because it allows to adapt the weights value from the global point of view, thus thin features will not be overlooked like in the conventional non-local models. Since now the non-local total variation term has two variables, the image u and weights v, and it is concave with respect to v, the proximal alternating linearized minimization algorithm is naturally applied with variable metrics to solve the non-convex model efficiently. In the meantime, the efficiency of the proposed approaches is demonstrated on problems including image segmentation, image inpainting and image denoising.
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Chen, Liyuan. "Variational approaches in image recovery and segmentation." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/227.

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Image recovery and segmentation are always the fundamental tasks in image processing field, because of their so many contributions in practical applications. As in the past ten years, variational methods have achieved a great success on these two issues, in this thesis, we continue to work on proposing several new variational approaches for restoring and segmenting an image. This thesis contains two parts. The first part addresses recovering an image and the second part emphasizes on segmenting. Along with the wide utilization of magnetic resonance imaging (MRI) technique, we particularly deal with blurry images corrupted by Rician noise. In chapter 1, two new convex variational models for recovering an image corrupted by Rician noise with blur are presented. These two models are motivated by the non-convex maximum-a-posteriori (MAP) model proposed in the prior papers. In the first method, we use an approximation item to the zero order of the modified Bessel function in the MAP model and add an entropy-like item to obtain a convex model. Through studying on the statistical properties of Rician noise, we bring up a strictly convex model by adding an additional data-fidelity term in the MAP model in the second method. Primal-dual methods are applied to solve the models. The simulation outcomes show that our models outperform some existed effective models in both recovery image quality and computational time. Cone beam CT (CBCT) is routinely applied in image guided radiation therapy (IGRT) to help patient setup. Its imaging dose, however, is still a concern, limiting its wide applications. It has been an active research topic to develop novel technologies for radiation dose reduction. In chapter 2, we propose an improvement of practical CBCT dose control scheme - temporal non-local means (TNLM) scheme for IGRT. We denoise the scanned image with low dose by using the previous images as prior knowledge. We combine deformation image registration and TNLM. Different from the TNLM, in the new method, for each pixel, the search range is not fixed, but based on the motion vector between the prior image and the obtained image. By doing this, it is easy to find the similar pixels in the previous images, but also can reduce the computational time since it does not need large search windows. The phantom and patient studies illuminate that the new method outperforms the original one in both image quality and computational time. In the second part, we present a two-stage method for segmenting an image corrupted by blur and Rician noise. The method is motivated by the two-stage segmentation method developed by the authors in 2013 and restoration method for images with Rician noise. First, based on the statistical properties of Rician noise, we present a new convex variant of the modified Mumford-Shah model to get the smooth cartoon part {dollar}u{dollar} of the image. Then, we cluster the cartoon {dollar}u{dollar} into different parts to obtain the final contour of different phases of the image. Moreover, {dollar}u{dollar} from the first stage is unique because of the convexity of the new model, and it needs to be computed only once whenever the thresholds and the number of the phases {dollar}K{dollar} in the second stage change. We implement the simulation on the synthetic and real images to show that our model outperforms some existed segmentation models in both precision and computational time
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Jitsuchon, Somchai. "Three applications of market incompleteness and market imperfection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0026/NQ38906.pdf.

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Wang, Ying, and 王瑩. "A study of mutual fund flow and market return volatility." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26843572.

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Zeng, Jingying. "Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942.

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Mazzotta, Stefano. "Three essays on volatility." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85189.

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This dissertation is in the form of one survey paper and three essays on the topic of volatility. The unifying feature that permeates the entire thesis is the focus on the measurement and use of conditional second moment of equities and currencies as a measure of risk for asset pricing and policy purposes in the context of international markets.
The survey examines selected papers from the international finance literature and from the volatility literature with a focus on the theoretical and empirical relationship between first and second unconditional and conditional moments of domestic and international asset returns. It then specifically proposes several areas for investigation related to international finance topics. The first essay investigates the importance of asymmetric volatility when computing the risk premium of international assets. The results indicate that conditional second moment asymmetry is significant and time-varying. They also show that, if the price of risk is time-varying, the world market and foreign exchange risk premia estimated without allowing for time-varying asymmetry are less consistent with the data. Furthermore, they imply that asymmetry is more pronounced when the business condition is such that investors require higher compensation to bear risk.
In the second essay we start from the consideration that financial decision makers often consider the information in currency option valuations when making assessments about future exchange rates. The purpose of this essay is then to systematically assess the quality of option based volatility, interval and density forecasts. We use a unique dataset consisting of over 10 years of daily data on over-the-counter currency option prices. We find that the implied volatilities explain a large share of the variation in realized volatility. Finally, we find that wide-range interval and density forecasts are often misspecified whereas narrow-range interval forecasts are well specified.
In the third essay we examine whether the information contained in various measures of correlation among exchange rates can be used to assess future currency co-movement. We compare option-implied correlation forecasts from a dataset consisting of over 10 years of daily data on over-the-counter currency option prices to a set of return-based correlation measures and assess the relative quality of the correlation forecasts. We find that while the predictive power of implied correlation is not always superior to that of returns based correlations measures, it tends to provide the most consistent results across currencies. Predictions that use both implied and returns-based correlations generate the highest adjusted R2's, explaining up to 42 per cent of the realized correlations.
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Veraart, Luitgard Anna Maria. "Mathematical models for market making, option pricing and systemic risk." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613365.

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Buchta, Christian, and Sara Dolnicar. "Learning by simulation. Computer simulations for strategic marketing decision support in tourism." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/1718/1/document.pdf.

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This paper describes the use of corporate decision and strategy simulations as a decision-support instrument under varying market conditions in the tourism industry. It goes on to illustrate this use of simulations with an experiment which investigates how successful different market segmentation approaches are in destination management. The experiment assumes a competitive environment and various cycle-length conditions with regard to budget and strategic planning. Computer simulations prove to be a useful management tool, allowing customized experiments which provide insight into the functioning of the market and therefore represent an interesting tool for managerial decision support. The main drawback is the initial setup of a customized computer simulation, which is time-consuming and involves defining parameters with great care in order to represent the actual market environment and to avoid excessive complexity in testing cause-effect-relationships. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Mkhwanazi, MA (Mpendulo Armstrong). "Efficient Monte Carlo simulations of pricing captions using Libor market models." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/9114.

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Includes bibliographical references.
The cap option (caption) is one of common European exotic options discussed in literature. This (interest rates) exotic option has no closed form solution and its accurate pricing and hedging in a volatile market is a challenge for traders. The reason for this is that, comparatively, the behaviour on an individual interest rate is more complex than that of a stock price. To price any interest rate product, it is essential to develop an interest rates model describing the behaviour of the entire zero coupon yield curve. The equity and yield curve, respectively, relate to the difference in the dynamics of a scalar variable and vector variable. Moreover, captions are second order with respect to the discount bonds in that they are options on caps (which are also options on bonds). These reasons make it of particular interest to study efficient numerical solutions to price captions. Monte Carlo simulation provides a simple method for pricing this option, and a suitable interest rate model to use is the Libor market model. The approach of describing the behaviour of the entire zero coupon yield curve, in the era post the 2007 credit crunch crisis, is what is called a standard single-curve market practice, and Part l of this work is based on it. . After introducing the framework for option pricing in the interest rate market, the theory and implementation procedure for Monte Carlo simulation using Libor market models is described. A detailed analysis of the results is presented together with a sensitivity analysis, and finally suggestions for efficient pricing of captions are given. In Part II we review the recent financial market evolution, triggered by the credit crunch crisis towards double-curve approach. Unfortunately, such a methodology is not easy to build. In practice an empirical approach to price and hedge interest rate derivatives has prevailed in the market. Future cash flows are generated through multiple forwarding yield curves associated to the underlying rate tenors, and their net present value is calculated through discount factors front a single discounting yield curve.
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Books on the topic "Market segmentation Mathematical models"

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Hruschka, Harald. Abgrenzung und Segmentierung von Markten auf der Grundlage unscharfer Klassifikationsverfahren. Thun: H. Deutsch, 1985.

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Stigler, Matthieu. Understanding the ADR premium under market segmentation. New Delhi: National Institute of Public Finance and Policy, 2010.

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Guo ji shi chang qu ge hua zhi shi zheng yan jiu. Taibei Shi: Cai tuan fa ren Zhonghua jing ji yan jiu yuan, 1986.

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Martin, Watts. The interrelationship between labour market segmentation and occupational sex segregation in Britain. [Newcastle, N.S.W.]: Employment Studies Centre, University of Newcastle, 1991.

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Metrick, Andrew. Price versus quantity: Market clearing mechanisms when sellers differ in quality. Cambridge, MA: National Bureau of Economic Research, 1996.

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Wedel, Michel. Market segmentation: Conceptual and methodological foundations. Boston: Kluwer Academic, 1998.

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, Mass: Marketing Science Institute, 1988.

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, MA: Marketing Science Institute, 1988.

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Lang, Kevin. Bilateral search as an explanation for labor market segmentation and other anomalies. Cambridge, MA: National Bureau of Economic Research, 1993.

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Bayus, Barry L. A segmentation model for the targeted marketing of consumer durables: Technical working paper. Cambridge, Mass: Marketing Science Institute, 1994.

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Book chapters on the topic "Market segmentation Mathematical models"

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Claas, Oliver. "Efficient Bargaining Under Labor Market Segmentation in a Macroeconomic Model." In Lecture Notes in Economics and Mathematical Systems, 109–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97828-4_4.

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Wählby, Carolina. "Image Segmentation, Processing and Analysis in Microscopy and Life Science." In Mathematical Models in Biology, 1–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_1.

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Reutter, Michael. "Labour Market Models and Wage Dynamics." In Lecture Notes in Economics and Mathematical Systems, 47–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18159-7_5.

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Hruschka, Harald, and Martin Natter. "Clustering-Based Market Segmentation Using Neural Network Models." In Operations Research Proceedings 1993, 268. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78910-6_94.

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Thoron, Sylvie. "Market Organization: Noncooperative Models of Coalition Formation." In Lecture Notes in Economics and Mathematical Systems, 207–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57005-6_10.

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Badshah, Noor. "Fast Numerical Methods for Image Segmentation Models." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 1–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-03009-4_121-1.

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Garibaldi, U., M. A. Penco, and P. Viarengo. "An Exact Physical Approach to Market Participation Models." In Lecture Notes in Economics and Mathematical Systems, 91–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55651-7_6.

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Stawiaski, Jean. "Optimal Path: Theory and Models for Vessel Segmentation." In Mathematical Morphology and Its Applications to Image and Signal Processing, 417–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21569-8_36.

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Ferrando, Sebastian, Alfredo Gonzalez, Ivan Degano, and Massoome Rahsepar. "Trajectory Based Market Models. Arbitrage and Pricing Intervals." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 99–103. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_23.

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Grindel, Ria, Wieger Hinderks, and Andreas Wagner. "Application of Continuous Stochastic Processes in Energy Market Models." In Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, 25–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62732-4_2.

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Conference papers on the topic "Market segmentation Mathematical models"

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Binti Omar, Nor Alwani, and Faridah Abdul Halim. "Modelling volatility of Malaysian stock market using garch models." In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC). IEEE, 2015. http://dx.doi.org/10.1109/ismsc.2015.7594096.

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Moldovanu, Simona, Luminita Moraru, and Dorin Bibicu. "Mathematical models used in segmentation and fractal methods of 2-D ultrasound images." In 9TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4765560.

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Huang, Ziye, Xiyue Wu, and Zehan Duan. "Tests of CBOE Options Market Efficiency and Arbitrage Opportunities Based on Options Pricing Mathematical Models." In 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID). IEEE, 2020. http://dx.doi.org/10.1109/msieid52046.2020.00041.

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Prastawa, Marcel, Suyash P. Awate, and Guido Gerig. "Building spatiotemporal anatomical models using joint 4-D segmentation, registration, and subject-specific atlas estimation." In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE, 2012. http://dx.doi.org/10.1109/mmbia.2012.6164740.

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Gaidelys, Vaidas, and Emilija Naudžiūnaitė. "EVALUATION OF THE MATHEMATICAL MODELLING METHODS AVAILABLE IN THE MARKET." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.725.

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The major purpose of this research is to analyse and select the relevant mathematical modelling methods that will be employed for developing an algorithm. To fulfil the major purpose, three following objectives were raised. First, to select and substantiate the most common mathematical modelling methods. Second, to test the pre-selected meth-ods under laboratory conditions so that the most relevant method for implementing the target project could be identi-fied. Third, to prepare at least 3 models for application. The research results indicate that when evaluating the respira-tory virus (SARS-CoV-2 causing COVID-19) concentration and survival rate dependence on a number of traits, the methods of descriptive statistics, confidence intervals, hypothesis testing, dispersion analysis, trait dependence analysis, and regression analysis are employed. All the above-listed methods were tested under laboratory conditions and thus can be applied to evaluate the effectiveness of the project product – a device designed to prevent transmission of res-piratory viruses through air droplets. Selection of a particular method depends on a set of traits to be analysed, a trait type (quantitative, qualitative), a trait distribution type, and parameters. In the context of COVID-19, there is an urgent need to bring new products to market. Since most of the new products developed are directly related to research, it is very important to calculate the algorithms required to provide the service. Therefore, in order to calculate the optimal algorithm, it is necessary to analyze the algorithms already on the market. In this way, the products developed can gain a competitive advantage over competitors’ products. Given that the equipment placed on the market will be equipped with HINS radiation sources, such a product will become original and new on the market. Therefore, it is necessary to evaluate several methods of mathematical modelling. It is also necessary to take into account that the placing on the market of a product takes place in the context of global competition.
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Jiang, Y., J. Meng, and N. Jaffer. "A Novel Segmentation and Navigation Method for Polyps Detection using Mathematical Morphology and Active Contour Models." In 6th IEEE International Conference on Cognitive Informatics. IEEE, 2007. http://dx.doi.org/10.1109/coginf.2007.4341910.

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Ashimov, Abdykappar A., Nurlan A. Iskakov, Yuriy V. Borovskiy, Bahyt T. Sultanov, and Askar A. Ashimov. "On the Development and Usage of the Market Economy Parametrical Regulation Theory on the Basis of One-Class Mathematical Models." In 2008 19th International Conference on Systems Engineering (ICSENG). IEEE, 2008. http://dx.doi.org/10.1109/icseng.2008.11.

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Klochko, Elena, and Kristina Karpenko. "Mathematical and Analytical Models of the Market of Commercial Real Estate: Monitoring, Analysis and Projected Growth in the Context of Clusterization." In 6th International Conference on Economics, Management, Law and Education (EMLE 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/aebmr.k.210210.062.

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A. Rendón, Manuel, André R. Novgorodcev, and Daniel De A. Fernandes. "Mathematical Model of a 106 MW Single Shaft Heavy-Duty Gas Turbine." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2279.

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In recent years, several thermal power plants were built in Brazil and the percentage of participation of this kind of power generation increased in the local energy market. Since the 1980's, several studies developed mathematical models for gas turbines to be applied in power system analysis. These are simplified representations of static and dynamic behavior of machines. However, published works in dynamic gas turbine models represent a narrow set of machines, and most of the applications in power system analysis employ them, despite the fact that they are not accurate representations of some specific machines. This work presents the modeling procedure and validation for a 106 MW heavy-duty gas turbine working in combined cycle in a Brazilian thermal power plant. The gray-box approach, based on an existing tuned model based on real sampled data, is used, and the modeling involves a static approach in steady state, and dynamic modeling with system identification from sampled data. Sampled data were corrected to standard environmental conditions. The model was developed and validated in MATLAB®-Simulink®.
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FREIMANIS, Kristaps, and Maija ŠENFELDE. "METHODOLOGY FOR THE ASSESSMENT OF REGULATION COSTS IN THE BANKING MARKET." In International Scientific Conference „Contemporary Issues in Business, Management and Economics Engineering". Vilnius Gediminas Technical University, 2021. http://dx.doi.org/10.3846/cibmee.2021.600.

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Purpose – In the field of the economics’ regulation researchers so far have built the conceptual framework showing how the deadweight loss of market failures decrease and costs of the government intervention increase with the increased level of the government intervention. In order to quantify relationships between the level of intervention, intervention costs and the deadweight loss with econometric models it is important to understand how to quantify the regulation costs as a part of intervention costs. The objective of the research presented in this paper is to find the appropriate methodology for the quantification of the regulation costs in the banking market. Research methodology – literature review (regarding theories), mathematical methods for quantification and econometric methods for validation purposes. Findings – research shows that in the assessment of regulation costs three main stakeholders should be included – microprudential regulator, macroprudential regulator and financial regulation’s policy maker. Research presents their cost assessment methodology. Its validation shows that in general methodology works as expected, i.e., higher government intervention levels lead to higher regulation costs, however this general rule has exceptions, which in authors’ view indicates that other factors have an impact on the cost levels. Research limitations – research shows how to assess the costs of main stakeholders based on the publicly available information. More precise view could be obtained if in the cooperation with authorities more details on certain cost items are received. Practical implications – research results will be used to assess all government intervention costs (other positions include compliance costs and other indirect costs) and finalize the quantification of the framework. Quantified framework could be used for more precise policy making regarding the regulation of the banking market. Originality/Value – research shows how to quantify the regulation costs of the banking market as currently there are only conceptual ideas.
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