scholarly journals The Option Value Model in the Retirement Literature: The Trade-Off between Computational Complexity and Predictive Validity

2012 ◽  
Author(s):  
Michele Belloni
Keyword(s):  
Computational Complexity ◽  
Predictive Validity ◽  
Option Value ◽  
Trade Off ◽  
Value Model

scholarly journals Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 957
Author(s):  
Branislav Popović ◽  
Lenka Cepova ◽  
Robert Cep ◽  
Marko Janev ◽  
Lidija Krstanović
Keyword(s):  
Computational Complexity ◽  
Parameter Space ◽  
Recognition Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Dimensional Manifold ◽  
Dimensional Parameter ◽  
Trade Off ◽  
Measure Of Similarity ◽  
Lower Dimensional

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

MINIMIZING THE COMPLETE INFLUENCE TIME IN A SOCIAL NETWORK WITH STOCHASTIC COSTS FOR INFLUENCING NODES

2013 ◽  
Vol 21 (supp01) ◽  
pp. 63-74 ◽  
Author(s):  
YAODONG NI ◽  
QIAONI SHI
Keyword(s):  
Social Network ◽  
Computational Complexity ◽  
Stochastic Programming ◽  
Greedy Algorithm ◽  
Random Graphs ◽  
Minimum Time ◽  
Programming Models ◽  
Decision Criteria ◽  
Trade Off ◽  
The Cost

In this paper, we study the problem of targeting a set of individuals to trigger a cascade of influence in a social network such that the influence diffuses to all individuals with the minimum time, given that the cost of initially influencing each individual is with randomness and that the budget available is limited. We adopt the incremental chance model to characterize the diffusion of influence, and propose three stochastic programming models that correspond to three different decision criteria respectively. A modified greedy algorithm is presented to solve the proposed models, which can flexibly trade off between solution performance and computational complexity. Experiments are performed on random graphs, by which we show that the algorithm we present is effective.

Essays on empirical models of retirement

2018 ◽  
Author(s):  
◽  
Wei Kong
Keyword(s):  
Public School ◽  
Peer Effects ◽  
Financial Incentives ◽  
Structural Model ◽  
Programming Model ◽  
Reduced Form ◽  
Option Value ◽  
Public School Teachers ◽  
School Teachers ◽  
Value Model

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] In this study, I examine effects of financial incentives and non-financial incentives on public school teachers' retirement in Missouri. For financial incentives, existing studies commonly model the retirement in two types, static reduced-form and structural-form. There are two main types of the reduced-form models, linear probability model (e.g., Costrell and McGee, 2010; Brown, 2013), probit or logit model (e.g., Furgeson et al., 2006; Asch et al., 2005; Coile and Gruber, 2007). A prominent structural form model is the Stock and Wise (1990) option value model. In addition, some researchers (Brown and Laschever, 2012; Gustman and Steinmeier, 2000; Dwyer and Mitchell, 1999) suggest that non-financial incentives, such as colleagues, health status, and family, are also important factors to explain retirement decisions. Among these non-financial incentives, peer effects are the focus of my study. The first chapter analyzes peer effects on public school teachers' retirement timing decisions. I use an administrative dataset of the full population of late career Missouri public school teachers during academic years 1994-2007 to construct a model incorporating both financial incentives and non-financial incentives when teachers make retirement decisions. My study mainly differs from previous literature in the controls of financial incentives and the use of data. For controls of financial incentives, instead of using pension wealth and peak value, I propose the simulated "financial benefit of postponing retirement" based on Stock-Wise (hereafter, SW) option value model as a new financial incentive proxy. Besides, I use a much richer dataset so that more detailed information about teachers are available and the schools are more diverse in poverty and urbanicity. Empirically, when the retirement rate of peers in previous year increases 1%, the probability of retirement increases approximately 0.128 percentage points. In addition, I find evidence of heterogeneous peer effects. For example, teachers with different education levels respond to peers retirement differently. Because the reduced-form parameters generally depend on the pension rules, the reduced-form models are unreliable to predict the effects of changes in the pension rules. In contrast, the structural model parameters are independent of pension rules, which makes them useful for policy analysis. The second chapter uses an economic structural model to fit late career Missouri public school teachers' data and study the effects of changing pension rules on the timing of retirement. Ni and Podgursky (2016) show that the SW option value model produces good fit for the Missouri public school teachers during 2002-2008. There are three limitations in their study and other applications of the SW model. First, the SW option value model has only been applied to retirement data when the pension rules are fixed. Second, there is a selection bias in the sample of senior teachers: among the retirement-eligible teachers only the "stayers" are in the sample, while the "early leavers" are absent. This bias has not been adjusted in the previous studies. Third, the SW model is estimated based on the likelihood of panel data of individuals (teachers or salesmen). The cost of computing the likelihood increases in the number of teachers and the length of the sample period. For a larger state, such as Texas, and/or a longer sample period, the computation cost may be prohibitively high. The second chapter solves these problems. First, I model expectations of pension rule changes. Second, I adjust the sample selection bias by using distribution of initial preference errors. Third, I group individual teachers into a fixed number of (age, experience) cells to reduce the time of evaluating the likelihood. I estimate the model under different expectation assumptions and compare their performance by simulation. The estimated SW model exhibits good in-sample and out-of-sample fits. Counterfactual analysis shows that pension enhancements reduce the retirement age by around 0.4 years for the 1994 cohort teachers and by more than one year for teachers in the steady state. Finally, I compare SW option value model with an alternative widely applied structural model of retirement, dynamic programming model. Compared to dynamic programming model, the SW model tends to predict early retirement.

scholarly journals Comparison of Models of Single-Phase Diode Bridge Rectifiers for Their Use in Harmonic Studies with Many Devices

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 66
Author(s):  
Tatiano Busatto ◽  
Sarah K. Rönnberg ◽  
Math H. J. Bollen
Keyword(s):  
Numerical Simulation ◽  
Computational Complexity ◽  
Time Domain ◽  
Single Phase ◽  
Low Voltage ◽  
Level Of Detail ◽  
Trade Off ◽  
Current Harmonics ◽  
Voltage Distortion ◽  
Conduction Mode

Harmonic modeling of low-voltage networks with many devices requires simple but accurate models. This paper investigates the advantages and drawbacks of such models to predict the current harmonics created by single-phase full-bridge rectifiers. An overview is given of the methods, limiting the focus to harmonic analysis. The error of each method, compared to an accurate numerical simulation model, is quantified in frequency and time domain considering realistic input scenarios, including background voltage distortion and different system impedances. The results of the comparison are used to discuss the applicability of the models depending on the harmonic studies scale and the required level of detail. It is concluded that all models have their applicability, but also limitations. From the simplest and fastest model, which does not require a numerical solution, to the more accurate one that allows discontinuous conduction mode to be included, the trade-off involves accuracy and computational complexity.

Conceptual Design As Novel Combination of Existing Device Features

1987 ◽  
Author(s):  
K. Ulrich ◽  
W. Seering
Keyword(s):  
Computational Complexity ◽  
Conceptual Design ◽  
Design Space ◽  
Effective Means ◽  
Trade Off ◽  
Computer Tools ◽  
Initial Stage ◽  
Design Languages ◽  
Structural Descriptions ◽  
Future Work

Abstract Conceptual design is the initial stage of the transformation between functional and structural descriptions of devices. Our primary aim is to develop ideas that will allow computer tools for conceptual design to be built. In this paper, we point out a fundamental trade-off between the expressiveness of design languages and the computational complexity of the resulting design problem. Research in computational conceptual design can therefore be viewed as the problem of defining a design language and then devising ways of controlling the size of the resulting design space. We propose that an effective means of controlling complexity is to use knowledge of existing designs to guide the synthesis of novel designs. We illustrate this concept with a program that designs novel mechanical fasteners from knowledge of existing fasteners. We analyze this experiment and highlight several areas for future work.

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