Relevant bibliographies by topics / Death – Causes – Econometric models

Academic literature on the topic 'Death – Causes – Econometric models'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Death – Causes – Econometric models"

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Bett, Nicholas, Juma Kasozi, and Daniel Ruturwa. "Temporal Clustering of the Causes of Death for Mortality Modelling." Risks 10, no. 5 (May 6, 2022): 99. http://dx.doi.org/10.3390/risks10050099.

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Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.
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Zittersteyn, Geert, and Jennifer Alonso-García. "Common Factor Cause-Specific Mortality Model." Risks 9, no. 12 (December 3, 2021): 221. http://dx.doi.org/10.3390/risks9120221.

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Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters.
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Stepanov, Vladimir S. "Assessing SARS-CoV-2-related mortality rate in Russian regions, based on the econometric model." Russian Journal of Infection and Immunity 12, no. 4 (September 9, 2022): 783–89. http://dx.doi.org/10.15789/2220-7619-asr-1846.

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The objects of the study were the daily data on the population morbidity and mortality due to coronavirus disease 2019 (COVID-19) in Russian regions, as well as regional medical, demographic and environmental data recorded in recent years. COVID-19 is a contagious disease caused by the novel coronavirus (SARS-CoV-2). The mathematical methods consist of correlation and regression analysis, methods of testing statistical hypotheses. First, a multiple Variable Structure Regression should be specified. The intercept in the model differs from region to region, depending on the combination of values for dummy variables. The role of the dependent variable Y t was chosen as the cumulative mortality published by the operational headquarters for the regions that has been linked to day t, so that COVID-19 was considered the main cause of death. The complex of explanatory variables included two factorial variables that changed daily, and had a lag relative to t value. Also, this complex included a number of variables that did not change with the growth of t: the explanatory variable with the regions availability with doctors of certain specialties; and four dummy variables. One of them coded the regions belonging to the two southern Russian Federal Districts. Three other variables characterized the increased air pollution in settlements recorded in recent years, as well as the level of radiation pollution of the regions territory and the population health estimated for 10 classes of diseases (for the circulatory system, endocrine system, etc.). The values of such dummy variables were obtained from open data from the Federal State Statistics Service (Rosstat) etc. The model parameters were estimated by the least squares method using the training table, which included 40 Russias regions, the t parameter for variable Y t was assessed starting from November, 1, 2021. As a result, a statistical model was built with an approximation error equal to 3%. For regions of the regions examined this error was 1.94 (1.5)% for the value Y t that has been fixed on the 1st Nov. The plots show daily prediction for mortality rate due to COVID-19 in the first half of November for seven Russian regions compared with actual data. The model can be useful in development of medical and demographic policy in geographic regions, as well as generating adjusted compartment models that based on systems of differential equations (SEIRF, SIRD, etc.).
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Babashova, Sakina. "Predicting the Dynamics of Covid-19 Propagation in Azerbaijan based on Time Series Models." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 18 (July 28, 2022): 1036–48. http://dx.doi.org/10.37394/232015.2022.18.99.

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The study is dedicated to developing an econometric model that can be used to make medium-term forecasts about the dynamics of the spread of the coronavirus in different countries, including Azerbaijan. We examine the number of COVID-19 cases and deaths worldwide to understand the data's intricacies better and make reliable predictions. Though it’s essential to quickly obtain an acceptable (although not perfect) prediction that shows the critical trends based on incomplete and inaccurate data, it is practically impossible to use standard SIR models of the epidemic spread. At the same time the similarity of the dynamics in different countries, including those which were several weeks ahead of Azerbaijan in the epidemic situation, and the possibility of including the heterogeneity factors into the model allowed as early as March 2020 to develop the extrapolation working relatively well on the medium-term horizon. The SARS-CoV-2 virus, which causes COVID-19, has affected societies worldwide, but the experiences have been vastly different. Countries' health-care and economic systems differ significantly, making policy responses such as testing, intermittent lockdowns, quarantine, contact tracing, mask-wearing, and social distancing. The study presented in this paper is based on the Exponential Growth Model method, which is used in statistical analysis, forecasting, and decision-making in public health and epidemiology. This model was created to forecast coronavirus spread dynamics under uncertainty over the medium term. The model predicts future values of the percentage increase in new cases for 1–2 months. Data from previous periods in the United States, Italy, Spain, France, Germany, and Azerbaijan were used. The simulation results confirmed that the proposed approach could be used to create medium-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Azerbaijan, the deviation of the predicted total number of confirmed cases from the actual number was within 3-10 percent. Based on March statistics on the spread of the coronavirus in the US, 4 European countries: Italy, Spain, France, Germany (most susceptible to the epidemic), and Azerbaijan, it was shown how the trajectory would deviate exponentially from a shape; a trial was carried out to identify and assess the key factors that characterize countries. One of the unexpected results was the impact of quarantine restrictions on the number of people infected. We also used the medium-term forecast set by the local government to assess the adequacy of health systems.
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Lambrecht, Stéphane. "Altruistic bequests and non-negative savings." Recherches économiques de Louvain 69, no. 4 (2003): 349–69. http://dx.doi.org/10.1017/s0770451800007910.

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SummaryThis paper builds on the class of models studying the game interaction between an altruistic benefactor and a selfish recipient. An altruistic parent's bequest is transferred to his selfish son after the former's death and we assume that it is not a valid collateral for bank loans. This is equivalent to adding a non-negativity constraint on savings to the standard bequest model. A crucial mechanism at work is that the son's choice of a level of action can seriously dwarf his budget set. When Becker's result holds, the credit constraint places an upper bound on the strategic savings of the Samaritan's dilemma type. But the constraint on savings also causes the shrinkage of the validity domain of the Rotten Kid Theorem because it may lead both poor and rich heirs to behave unoptimally from the family point of view.
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Mur, Jesús, Ana M. Angulo, and Fernando A. López. "Looking for the Causes of Instability in Spatial Econometric Models." International Regional Science Review 35, no. 3 (June 15, 2012): 303–38. http://dx.doi.org/10.1177/0160017611415265.

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Doszyń, Mariusz. "Determination of Impact of Propensities by Means of Residuals of Econometric Models for Spatial Data." Folia Oeconomica Stetinensia 8, no. 1 (January 1, 2009): 49–56. http://dx.doi.org/10.2478/v10031-009-0023-5.

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Determination of Impact of Propensities by Means of Residuals of Econometric Models for Spatial Data It is important to be aware that economic occurrences depend also on subjective (psychological and sociological) factors. In many cases these causes could be identified with propensities. Propensities could be understood as a generalized psychological and sociological causes that make probabilities of certain events higher in given objective circumstances. In the article proposition of determining impact of propensities on economic phenomena by means of residuals of econometric models for spatial data was discussed. Econometric consequences of omitting subjective factors (propensities) while analyzing socio-economic regularities were presented. Such kind of residuals as OLS, predictive, studentized, recursive and BLUS residuals were described. Econometric properties of mentioned residuals were also pointed out. In the empirical example all types of residuals were used to analyze impact of propensity to consume in chosen European countries in 2006.
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Wan Yaacob, Wan Fairos, and Wan Zakiyatussariroh Wan Husin. "Modelling Malaysian Road Accident Deaths : An Econometric Approach." Social and Management Research Journal 3, no. 1 (June 1, 2006): 99. http://dx.doi.org/10.24191/smrj.v3i1.5105.

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A number of methods have been proposed for dealing with road accident death model. This paper uses econometric regression models to develop the road accident death model. By using this approach, this paper attempts to establish a statistical model to describe the relationship between the total road accident deaths and a range ofexplanatory macroeconomic variables. The macroeconomic factors used in the model include population, the number of registered vehicles, road length, technique of data coverage, system of data recording and Gross Domestic Product. The results suggest that the POp, ROADL, VEH and DR do not have any impact on road accident deaths. In contrast, the GDP and Technique of data Coverage were found to be highly significant (P < 0.05) in explaining the road accident deaths.
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Lazarides, Themistokles, Evaggelos Drimpetas, and George Kyriazopoulosr. "Mergers, liquidations and bankruptcies in the European banking sector." Risk Governance and Control: Financial Markets and Institutions 5, no. 2 (2015): 52–70. http://dx.doi.org/10.22495/rgcv5i2art6.

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The inactivity of banks may be the result of a number of events, such as merger & acquisition (M&A), liquidation, default-bankruptcy, etc. All these phenomena of inactivity contribute to the same result, the reform of the European banking sector and they may have the same causes. The paper will address the issue of inactivity and will try to detect its causes using econometric models. Six groups of indicators are examined: performance, size, ownership, corporate governance, capital adequacy or capital structure and loan growth. Three econometric methods (Probit, Logit, OLS) have been used to create a system that predicts inactivity. The results of the econometric models show that from the six groups of indicators, four have been found to be statistically important (performance, size, ownership, corporate governance). Two have a negative impact (ownership, corporate governance) on the probability of inactivity and two positive (performance, size). The paper’s value and innovation is that it has given a systemic approach to find indicators of inactivity and it has excluded two groups of indicators as non-statistically important (capital adequacy or capital structure and growth).
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Sandoval-Pineda, Alejandro, Cesar Pedraza, and Aquiles E. Darghan. "Macroscopic Spatial Analysis of the Impact of Socioeconomic, Land Use and Mobility Factors on the Frequency of Traffic Accidents in Bogotá." Computers 11, no. 12 (December 8, 2022): 180. http://dx.doi.org/10.3390/computers11120180.

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The urban structure of a city, defined by its inhabitants, daily movements, and land use, has become an environmental factor of interest that is related to traffic accidents. Traditionally, macro modeling is usually implemented using spatial econometric methods; however, techniques such as support vector regression have proven to be efficient in identifying the relationships between factors at the zonal level and the frequency associated with these events when considering the autocorrelation between spatial units. As a result of this, the main objective of this study was to evaluate the impact of socioeconomical, land use, and mobility variables on the frequency of traffic accidents through the analysis of area data using spatial and vector support regression models. The spatial weighting matrix term was incorporated into the support vector regression models to compare the results against those that ignore it. The urban land of Bogotá, disaggregated into the territorial units of mobility analysis, was delimited as a study area. Two response variables were used: the traffic accidents index on the road perimeter and the traffic accidents index with deaths on the road perimeter, to analyze the total number of traffic accidents and the deaths caused by them. The results indicated that the rate of trips per person by taxi and motorcycle had the greatest impact on the increase in total accidents and deaths caused by them. Support vector regression models that incorporate the spatial structure allowed the modeling of the spatial dependency between spatial units with a better fit than the spatial regression models.
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Dissertations / Theses on the topic "Death – Causes – Econometric models"

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Espino, Elio R. "Investigation of the causes of serious injury and fatal crashes : an analysis using econometric models." FIU Digital Commons, 2005. http://digitalcommons.fiu.edu/etd/3244.

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The rate of fatal crashes in Florida has remained significantly higher than the national average for the last several years. The 2003 statistics from the National Highway Traffic Safety Administration (NHTSA), the latest available, show a fatality rate in Florida of 1.71 per 100 million vehicle-miles traveled compared to the national average of 1.48 per 100 million vehicle-miles traveled. The objective of this research is to better understand the driver, environmental, and roadway factors that affect the probability of injury severity in Florida. In this research, the ordered logit model was used to develop six injury severity models; single-vehicle and two-vehicle crashes on urban freeways and urban principal arterials and two-vehicle crashes at urban signalized and unsignalized intersections. The data used in this research included all crashes that occurred on the state highway system for the period from 2001 to 2003 in the Southeast Florida region, which includes the Miami-Dade, Broward and Palm Beach Counties. The results of the analysis indicate that the age group and gender of the driver at fault were significant factors of injury severity risk across all models. The greatest risk of severe injury was observed for the age groups 55 to 65 and 66 and older. A positive association between injury severity and the race of the driver at fault was also found. Driver at fault of Hispanic origin was associated with a higher risk of severe injury for both freeway models and for the two-vehicle crash model on arterial roads. A higher risk of more severe injury crash involvement was also found when an African-American was the at fault driver on two-vehicle crashes on freeways. In addition, the arterial class was also found to be positively associated with a higher risk of severe crashes. Six-lane divided arterials exhibited the highest injury severity risk of all arterial classes. The lowest severe injury risk was found for one way roads. Alcohol involvement by the driver at fault was also found to be a significant risk of severe injury for the single-vehicle crash model on freeways.
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Duong, Lien Thi Hong. "Australian takeover waves : a re-examination of patterns, causes and consequences." UWA Business School, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0201.

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This thesis provides more precise characterisation of patterns, causes and consequences of takeover activity in Australia over three decades spanning from 1972 to 2004. The first contribution of the thesis is to characterise the time series behaviour of takeover activity. It is found that linear models do not adequately capture the structure of merger activity; a non-linear two-state Markov switching model works better. A key contribution of the thesis is, therefore, to propose an approach of combining a State-Space model with the Markov switching regime model in describing takeover activity. Experimental results based on our approach show an improvement over other existing approaches. We find four waves, one in the 1980s, two in the 1990s, and one in the 2000s, with an expected duration of each wave state of approximately two years. The second contribution is an investigation of the extent to which financial and macro-economic factors predict takeover activity after controlling for the probability of takeover waves. A main finding is that while stock market boom periods are empirically associated with takeover waves, the underlying driver is interest rate level. A low interest rate environment is associated with higher aggregate takeover activity. This relationship is consistent with Shleifer and Vishny (1992)'s liquidity argument that takeover waves are symptoms of lower cost of capital. Replicating the analysis to the biggest takeover market in the world, the US, reveals a remarkable consistency of results. In short, the Australian findings are not idiosyncratic. Finally, the implications for target and bidder firm shareholders are explored via investigation of takeover bid premiums and long-term abnormal returns separately between the wave and non-wave periods. This represents the third contribution to the literature of takeover waves. Findings reveal that target shareholders earn abnormally positive returns in takeover bids and bid premiums are slightly lower in the wave periods. Analysis of the returns to bidding firm shareholders suggests that the lower premiums earned by target shareholders in the wave periods may simply reflect lower total economic gains, at the margin, to takeovers made in the wave periods. It is found that bidding firms earn normal post-takeover returns (relative to a portfolio of firms matched in size and survival) if their bids are made in the non-wave periods. However, bidders who announce their takeover bids during the wave periods exhibit significant under-performance. For mergers that took place within waves, there is no difference in bid premiums and nor is there a difference in the long-run returns of bidders involved in the first half and second half of the waves. We find that none of theories of merger waves (managerial, mis-valuation and neoclassical) can fully account for the Australian takeover waves and their effects. Instead, our results suggest that a combination of these theories may provide better explanation. Given that normal returns are observed for acquiring firms, taken as a whole, we are more likely to uphold the neoclassical argument for merger activity. However, the evidence is not entirely consistent with neo-classical rational models, the under-performance effect during the wave states is consistent with the herding behaviour by firms.
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Cotton, Christopher David. "Low Inflation: Potential Causes, Effects and Solutions." Thesis, 2019. https://doi.org/10.7916/d8-tg4q-7n86.

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My dissertation focuses upon low inflation. Many developed countries, especially Japan and the Eurozone, have recently experienced prolonged periods of below-target inflation. This has been blamed for many economic ills including worsening the Great Recession and generating a slow recovery, making monetary policy ineffective and leading to lower labor market flexibility. I study what has caused low inflation, its potential effects and how it could be prevented. In Chapter 1, I look at how effective raising the inflation target would be in mitigating the problems of low inflation. Many economists have proposed raising the inflation target to reduce the probability of hitting the zero lower bound (ZLB). It is both widely assumed and a feature of standard models that raising the inflation target does not impact the equilibrium real rate. I demonstrate that once heterogeneity is introduced, raising the inflation target causes the equilibrium real rate to fall in the New Keynesian model. This implies that raising the inflation target will increase the nominal interest rate by less than expected and thus will be less effective in reducing the probability of hitting the ZLB. The channel is that a rise in the inflation target lowers the average markup by price rigidities and a fall in the average markup lowers the equilibrium real rate by household heterogeneity which could come from overlapping generations or idiosyncratic labor shocks. Raising the inflation target from 2% to 4% lowers the equilibrium real rate by 0.38 percentage points in my baseline calibration. I also analyse the optimal inflation level and provide empirical evidence in support of the model mechanism. In Chapter 2, I study to what degree the recent fall in inflation can explain the rise in firm profitability which has been blamed for a rise in inequality. A theoretical relationship between inflation and profitability is known to exist. I investigate the degree to which the recent fall in inflation can explain the rise in firm profitability. My three primary findings are: 1. The negative relationship between inflation and profitability does not hinge upon the Calvo assumption. Raising inflation significantly lowers profitability under all common price rigidities. The relationship can actually be significantly stronger under menu costs. 2. A rise in the degree to which firms discount the future magnifies the effect; a rise in elasticity of substitution can increase or decrease the effect depending upon the price rigidity. 3. The profit share has risen by around 3.5p.p. since the 1990s. In a richer model with firm heterogeneity, the recent fall in inflation is estimated to explain 14% of the rise. This can increase to 29% if firms are allowed to discount the future by more in line with estimates from the finance literature. I also provide empirical evidence for the negative relationship between inflation and firm profits. In Chapter 3, I examine whether behavioral features can help to explain why some countries have persistently experienced low inflation at the zero lower bound. Economists are keen to introduce behavioral assumptions into modern macroeconomic models. A popular framework for doing so is sparse dynamic programming, which assumes that agents partly base their expectations upon a default model which is typically the steady state. This means agents' expectations will be wrong if there are long-run deviations from the default model and assumes agents can compute the default. I introduce an alternative form of sparse dynamic programming which tackles these problems by allowing for long-run updating to the behavioral part of agents' expectations. I apply this to derive a long-run behavioral New Keynesian model. Within this model, fixed interest rates yield indeterminacy and the costs of remaining at the zero lower bound are unbounded. These results are very different to a behavioral New Keynesian model based upon standard sparse dynamic programming, which can yield determinacy under fixed interest rates and bounded costs of the zero lower bound.
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Andreeva, Elena [Verfasser]. "Mortality due to external causes of death in the russian federation : spatial aspects and explanatory models / vorgelegt von Elena Andreeva." 2005. http://d-nb.info/978432118/34.

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Books on the topic "Death – Causes – Econometric models"

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Bhattacharya, Jay. Cause-specific mortality among medicare enrollees. Cambridge, Mass: National Bureau of Economic Research, 1996.

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Bhattacharya, Jay. Cause-specific mortality among medicare enrollees. Cambridge, MA: National Bureau of Economic Research, 1996.

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Robin, Sickles, and Taubman Paul 1939-, eds. Causes, correlates and consequences of death among older adults: Some methodological approaches and substantive analyses. Boston, Mass: Kluwer Academic Publishers, 1998.

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Breedon, F. J. M0: Causes and consequences. London: Bank of England, 1993.

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Engel, Charles. Deviations from purchasing power parity: Causes and welfare costs. Washington, D.C: Federal Reserve Board, 2000.

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R, Link Charles, and Staten Michael E, eds. Causes of litigation in workers' compensation. Kalamazoo, Mich: W.E. Upjohn Institute for Employment Research, 1995.

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Globalization of international financial markets: Causes and consequences. Aldershot, Hants, England: Ashgate, 1999.

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Temin, Peter. The causes of American business cycles: An essay in economic historiography. Cambridge, MA: National Bureau of Economic Research, 1998.

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Tremblay, Pierre. Crime and destiny: Hazard models for offenders. Montréal: Université de Montréal, 2000.

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Abel, Andrew B. Birth, death and taxes. Cambridge, MA: National Bureau of Economic Research, 1989.

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Book chapters on the topic "Death – Causes – Econometric models"

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Küfeoğlu, Sinan. "SDG-2 Zero Hunger." In Emerging Technologies, 209–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07127-0_4.

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AbstractPeople’s lives, communities and civilisations have all been defined by constant danger. Hunger is the menace, a plague that causes weakness, despair and death in the worst-case scenarios. One of the primary common threads has been hunger throughout history, which has resulted in large-scale migration, wars, conflicts and great sacrifices. This chapter presents the business models of 40 companies and use cases that employ emerging technologies and create value in SDG-2, Zero Hunger. We should highlight that one use case can be related to more than one SDG and it can make use of multiple emerging technologies.
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Winkle, Thomas. "Findings from Traffic Accident Analysis." In Product Development within Artificial Intelligence, Ethics and Legal Risk, 7–43. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-34293-7_2.

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AbstractWith regard to safe product development in the dilemma between Artificial Intelligence, ethics and legal risk, Thomas Winkle provides a meta-analysis for safety assessment using accident data to demonstrate potential safety benefits and risks. Thomas Winkle also refers to the disasters of the Chernobyl and Fukushima nuclear power plant accidents. They mark changes in acceptance about the use of nuclear energy. Comparisons are made between the global mortality rates of females and males on traffic mortality in relation to the life expectancy of various countries around the world, from Sierra Leone with the lowest life expectancy to Japan with the highest. The probability of dying from a traffic accident is highest in Venezuela and Sierra Leone. Another overview addresses the global mortality rate with exemplary causes of death in contrast to the ISO 26262 requirements of the Automotive Safety Integrity Level "ASIL D" and a hardware failure rate of less than 1 * 10-81/h. Furthermore, he uses traffic accident data examples of safety-enhancing automated vehicle systems with a low degree of automation that are already available on the market. For testing methods to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining worldwide traffic accident-, weather-, vehicle operation data and traffic simulations. Based on these findings, a realistic evaluation of internationally prospective, and statistically relevant real-world traffic scenarios as well as error processes and stochastic models can be analyzed (in combination with virtual tests in laboratories and driving simulators) to control critical driving situations.
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Snyder, Jessica M., Alessandro Bitto, and Piper M. Treuting. "Determining Cause of Death and Contributing Causes of Death in Rodent Aging Studies." In Conn's Handbook of Models for Human Aging, 195–209. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-811353-0.00016-6.

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Kapur, Navneet, and Robert Goldney. "What causes suicidal behaviour?" In Suicide Prevention, 24–32. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198791607.003.0004.

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This chapter discusses some of the causes of suicidal behaviour and some of the risk and protective factors. Psychological autopsy studies (which collect information from family members and other sources following suicide) suggest that as many as 80–90 per cent of people may have a psychiatric illness at the time of death. However, the causes of suicide are multiple and complex. Previous suicidal behaviour is also a very important risk factor. A number of models seek to bring together risk factors to further our understanding. One example is the stress diathesis model which highlights the interaction between individual vulnerability and later stressors. The Haddon Matrix on the other hand takes a public health perspective. The presence of even multiple risk factors is not sufficient to explain suicide and an individualized approach is needed when assessing patients.
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Harding, Don, and Adrian Pagan. "Using the Recurrent Event Binary States to Examine Economic Modeling Issues." In The Econometric Analysis of Recurrent Events in Macroeconomics and Finance. Princeton University Press, 2016. http://dx.doi.org/10.23943/princeton/9780691167084.003.0008.

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This chapter looks at using the binary states describing the recurrent events to help in either constructing economic models of time series or evaluating the fit of such models. The chapter provides a general discussion of the issues that come up when using the binary states in regressions. It then turns to the analysis of complete economic models. In these it is very common to see variance decompositions computed and used to draw conclusions about which shocks are responsible for the recurrent events. It is shown that this methodology is flawed when it comes to shedding light on what causes the business cycle. What can be done is investigated in the chapter, which illustrates how to determine which shocks are important to a matching of the business cycle features discussed in Chapter 5. The discussion moves on to some economic models that have been constructed in the wake of the global financial crisis and which aim to highlight the role of financial shocks.
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Quarrell, Oliver. "What causes selective nerve cell damage?" In Huntington's Disease, 108–18. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844389.003.0011.

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This chapter describes some of the work which has been undertaken to understand the cause of the selective nerve cell death which occurs in Huntington’s disease (HD). It has long been recognized that the change in the gene results in the protein product doing something which it should not: this is called a gain of function mutation. Despite the identification of the gene nearly 30 years ago a coherent story of the cause of the neurodegeneration has not been established. The chapter describes some of the animal models which have been developed and also the neuronal inclusions or aggregates which develop in cells.
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Berryman, Alan A. "Population Cycles Causes and Analysis." In Population Cycles. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195140989.003.0005.

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Ever since Elton’s classic book Voles, Mice and Lemmings (Elton 1942), understanding and explaining the causes of regular multiannual cycles in animal populations has been a central issue in ecology. Many hypotheses have been erected and incessantly argued about, but no clear picture has emerged. Below I briefly sketch the major hypotheses without any attempt to be complete or to comment on their relative merits or demerits. Detailed reviews and discussion can be found in Keith (1963), Krebs and Myers (1974), Finerty (1980), Myers (1988), Royama (1992), and Stenseth (1999). (H1) Physical effects (e.g., Elton 1924, Bodenheimer 1938). Perhaps the most obvious hypothesis is that cycles in animal populations reflect the response of birth and death rates to an external physical factor that is itself cyclic. Two of the more specific physical hypotheses involve periodic climatic factors and sunspot activity. (H2) Predator effects. Lotka (1924) and Volterra (1926) demonstrated that cyclic dynamics are inherent in simple predator-prey models, leading to the hypothesis that regular cycles can result from interactions between predator and prey populations. (H3) Pathogen effects. Anderson and May (1980) showed that, under certain conditions, simple models of infectious disease transmission can generate cycles in host and pathogen populations. This is similar to H2 with the pathogen as a predator. (H4) Plant effects. Several hypotheses have been proposed for the possible role of plants in generating population cycles of herbivores. One is a generalization of H2 in which the plant is considered the prey and the herbivore the predator (Elton 1924, Pitelka 1957). Another involves nutrient cycling: In this hypothesis, nutrient deficiencies are assumed to reduce the resistance of plants, resulting in larger herbivore populations, but nutrients released in feces and decaying animal and plant matter cycle back to the plants, increasing their vigor and resistance, and resulting in reduced herbivory (e.g., White 1974). Another hypothesis argues that herbivore feeding induces sustained chemical and/or physical changes in the plant (delayed induced resistance), which then reduce the reproduction and/or survival of future herbivore generations (Benz 1974, Haukioja and Hakala 1975).
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Rodrigues, Andreia, Domingos Xavier Viegas, José Zêzere, Aldina Santiago, and Luís Laím. "Rural fires – Causes of human losses in Portugal." In Advances in Forest Fire Research 2022, 757–66. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_116.

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Forest fires are the cause of many fatalities in Portugal and around the world. Extreme fire phenomena are increasingly, giving rise to intense and uncontrolled fires, with dimension and destructive potential greater than what is used to seeing and dealing with. The occupants of rural areas have a history of peaceful coexistence with fire, and they consider be prepared. However, with the abandonment of rural areas, abandonment of the agricultural activities that were carried out around the villages and the elderly populations, mean that villages are no longer naturally protected and fires get closer to the houses with greater intensity. It is crucial to protect rural populations that are vulnerable to wildfire threat. 2017 in Portugal comes with a milestone in history due to the high number of fatalities, in two different occurrences spaced in time, but geographically close: about 92% of deaths occurred in urban forest interface areas. Analysing the location of the mortal victims, it is possible to identify risk factors that lead to the death of civilians in the face of rural fires, such as the increase in demand for rurality, both for permanent and tourist housing, of inhabitants of urban areas, less used to the reality of rural fires, the false sensation of security that older populations will be able to due to changes in land use processes, making these populations even more vulnerable and, not least, the lack of preparation and awareness make the population wait for the fire to arrive at the homes. Population still has a poor perception of fire risk, making wrong last-minute decisions that sometimes lead to death. It is imperative to create models and protection mechanisms for populations in the urban-forest interface environments. This paper presents an analysis of the characteristics and causes of death of the victims of the June and October 2017 fires in Portugal, recognizing risk factors that led to their death.
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Lalwani, Kiran, Caroline French, and Christine Richardson. "Mouse Models to Understand Mutagenic Outcomes and Illegitimate Repair of DNA Damage." In Mutagenesis and Mitochondrial-Associated Pathologies [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103929.

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Maintenance of genome integrity is critical to prevent cell death or disease. Illegitimate repair of chromosomal DNA breaks can lead to mutations and genome rearrangements which are a well-known hallmark of multiple cancers and disorders. Endogenous causes of DNA double-strand breaks (DSBs) include reactive oxygen species (ROS) and replication errors while exogenous causes of DNA breaks include ionizing radiation, UV radiation, alkylating agents, and inhibitors of topoisomerase II (Top2). Recent evidence suggests that a growing list of environmental agents or toxins and natural dietary compounds also cause DNA breaks. Understanding the consequences of exposure to a broad spectrum of DSB-inducing agents has significant implications for understanding mutagenicity, genome stability and human health. This chapter will review in vivo mouse models designed to measure DNA damage and mutagenicity, and illegitimate repair of DNA DSBs caused by exposure to environmental agents.
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Zheng, Huilin, Syed Waseem Abbas Sherazi, Sang Hyeok Son, and Jong Yun Lee. "A Deep Neural Network Model for the Prediction of Major Adverse Cardiovascular Event Occurrences in Patients with non-ST-Elevation Myocardial Infarction." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210172.

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Cardiovascular disease (CVD) is one of the major causes of death all over the world and the mortality rate is higher than other causes. Hence, we propose a novel deep neural network (DNN)-based prediction model for the major adverse cardiovascular event (MACE) occurrences in patients with non-ST-Elevation myocardial infarction (NSTEMI) to improve the prediction accuracy of CVD. The research contents are described as follows. First, for the experiment, we use the Korean Acute Myocardial Infarction Registry (KAMIR-NIH) dataset with 2 years follow-ups and then preprocess the extracted data, such as processing the missing values, solving the imbalance problem, and applying the normalization meth to scale all the datasets in the same range for the experiment. Then we design a DNN-based prognosis model for the occurrences of MACE in NSTEMI patients. Finally, we evaluate the proposed model’s performance and compare it with several applied machine learning algorithms, such as logistic regression, K-Nearest Neighbors, decision tree, and support vector machine. The result shows that the performance of our proposed method outperformed other machine learning-based prediction models.
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Conference papers on the topic "Death – Causes – Econometric models"

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Wright, April. "INAPPROPRIATE PARAMETERIZATION OF FOSSILIZED BIRTH-DEATH MODELS CAUSES INCORRECT ESTIMATION OF NODE AGES." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-286597.

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Blair, Christopher A., Hanze Hu, Tim Huynh, Maggie Wu, Chung S. Yang, and Xiaolin Zi. "Abstract 1255: Delta-tocopherol induced endoplasmic reticulum stress causes autophagic degradation of ER and cell death in bladder cancer models." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-1255.

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Chen, Jenny, Jessica S. Coogan, Hyun Jin Kim, and Charles A. Taylor. "Pressure and Flow Characterization for Different Idealized Models of Stenotic Coronary Arteries." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19329.

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Coronary artery disease is one of the leading causes of death worldwide. This year, an estimated 785,000 Americans will have a new coronary attack [1]. Treatment of coronary artery disease varies from medication alone to cardiovascular surgery. Treatment regimens depend on the number and hemodynamic significance of stenoses present. In this study, we created idealized models of stenotic coronary arteries with varying geometric properties and simulated flow and pressure in coronary stenoses using a novel computational method including a three-dimensional model of an idealized coronary artery coupled to a lumped parameter model of the heart at the inlet and a lumped parameter model of the distal coronary vasculature at the outlet.
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Ye, Xiang S., Li Fan, Robert Van Horn, Tinggui Yin, Ryuichiro Nakai, Yoshihisa Ohta, Kelly Credille, et al. "Abstract A62: A novel Eg5 inhibitor that causes mitotic arrest leading to rapid cancer cell death shows broad‐spectrum antitumor activity in preclinical xenograft tumor models." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics--Nov 15-19, 2009; Boston, MA. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/1535-7163.targ-09-a62.

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Jahanyfard, E., B. Firoozabadi, and A. Goodarzvand Chegini. "Computational Simulation of Non-Newtonian Blood Flow in Carotid Bifurcation for Investigating the Various Rheological Blood Models." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42692.

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One of the leading causes for death after heart diseases and cancer in all over the world is still stroke. Most strokes happen because an artery carrying blood from the heart to the brain is clogged. Most of the time, as with heart attacks, the problem is atherosclerosis, hardening of the arteries, calcified build up of fatty deposits on the vessel wall. The primary troublemaker is the carotid artery, one on each side of the neck, the main thoroughfare for blood to the brain. In this study, the fluid dynamic simulations were done in the carotid bifurcation artery for studying the formation of atherosclerosis, and shear thinning behavior of blood as well as Newtonian comportment was studied. Under the steady flow conditions, Reynolds numbers representing the steady flow were under 1700. A comparison between rheological models for investigation each non-Newtonian model was carried out, velocity and wall shear stress distributions and its effect on developing atherosclerosis was studied; also the effect of non-Newtonian entrance length through this problem was exhibited.
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Wortmann, Nadine, Helena Guerreiro, Anna Kyselyova, Andreas M. Frölich, Jens Fiehler, and Dieter Krause. "Development and Manufacturing of Cervical Stenosis Models for the Integration Into a Neurointerventional Simulation Model." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71030.

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Abstract Ischemic stroke is one of the main causes of death and disability worldwide. This can be caused directly by vessel occlusions or secondarily, through constrictions, so-called stenoses. It is therefore important to treat the stenosis as quickly as possible in order to minimize long term patient disability. Since endovascular stroke treatment is a complicated procedure, performed under high time pressure, the establishment of an appropriate training environment is of particular importance. The aim of this study is the development and manufacturing of cervical stenosis models to simulate the endovascular treatment in a physical stroke training and research model to replace training on animal models. Additive Manufacturing (AM) is used to produce the models, as its geometric freedom makes it possible to assemble complicated anatomical shapes. In a series of tests in a full physical simulation environment with medical professionals, the concept of an outer shell held together with two snap fasteners or by Dual Lock™ velcro was shown to be suitable. For the fabrication of the vascular models, the Formlabs material Flexible 80 A Resin proved to be more promising than the Elastic Resin, as the latter ruptured too easily due to the compression in the molded shell.
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Zhao, Haifeng, Changxin Lai, Ke Wang, Suhao Qiu, Tianyao Wang, Wenheng Jiang, Jun Liu, Xiangdong Li, Jianfeng Zeng, and Yuan Feng. "Simulation of Mouse Brain Tissue Under Controlled Cortical Impact." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88790.

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Traumatic brain injury is one of the leading causes of injury and death in both developed and developing countries. Animal models are important preclinical tools for injury level studies. In this study, a finite element (FE) model of mouse brain was constructed to investigate the biomechanical responses of brain tissue during a controlled cortical impact (CCI). Impact of the brain tissue was simulated with varying impact speeds and angles. Computational results indicated that the viscoelastic properties of the brain tissue and the impact angle could greatly influence the injury responses. Comparison with the experimental observation showed that energy based stress parameters such as the von Mises stress has the potential to be descriptive of the injury levels.
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Da Silva, Andrio Rodrigo Corrêa, Iális Cavalcante de Paula Júnior, and Márcio André Baima Amora. "Breast cancer detection in histopathological images using convolutional neural networks." In Anais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcas.2019.6237.

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Breast cancer is one of the biggest causes of death among women around the world. Diagnosing this disease early can offer better treatment to the patient. Intelligent systems have been used for the detection of diseases using images. In this work a convolutional neural network was used for the detection of breast cancer in histopathological images through Keras library and TensorFlow framework. Models were created for 4 datasets with different magnifying factors (40x, 100x, 200x and 400x). Using k-fold cross-validation, it was found that there was a better result for the set of 400x images with 98.44% accuracy in the training data. The set of 200x images obtained a better result for recall and f1-score.
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Alves, Regina Reis da Costa, Frederico Caetano Jandre de Assis Tavares, José Manoel Seixas, and Anete Trajman. "Introducing lifelong machine learning in the active tuberculosis classification through chest radiographs." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-119.

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Tuberculosis (TB) is a contagious disease which is among the top 10 causes of death in the world. In order to eliminate the disease by 2050, the treatment of TB infection (TBI) is essential, which requires radiological reports to exclude active tuberculosis. The automatic X-ray classifiers used today are based on models that do not guarantee the retention of knowledge if they need to learn new tasks over time. This work proposes the introduction of the lifelong machine learning (LML) paradigm in automatic X-ray classifiers aimed at helping to diagnose active TB (ATB). Two LML algorithms, Efficient Lifelong Learning Algorithm (ELLA) and Learning without Forgetting (LwF), are applied to the TB and pneumonia classification tasks. The results show that it is possible to keep the performance in both tasks with the LML paradigm.
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Nafa, Fatema, Enoc Gonzalez, and Gurpreet Kaur. "An Approach using Machine Learning Model for Breast Cancer Prediction." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121815.

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Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.
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