Social influence

Summary

Social influence comprises the ways in which individuals adjust their behavior to meet the demands of a social environment. It takes many forms and can be seen in conformity, socialization, peer pressure, obedience, leadership, persuasion, sales, and marketing. Typically social influence results from a specific action, command, or request, but people also alter their attitudes and behaviors in response to what they perceive others might do or think. In 1958, Harvard psychologist Herbert Kelman identified three broad varieties of social influence. #Compliance is when people appear to agree with others but actually keep their dissenting opinions private. #Identification is when people are influenced by someone who is liked and respected, such as a famous celebrity. #Internalization is when people accept a belief or behavior and agree both publicly and privately. Morton Deutsch and Harold Gerard described two psychological needs that lead humans to conform to the expectations of others.

Official source

https://en.wikipedia.org/wiki/Social_influence

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In this thesis, we treat robust estimation for the parameters of the Ornstein–Uhlenbeck process, which are the mean, the variance, and the friction. We start by considering classical maximum likelihood estimation. For the simulation study, where we also investigate the choice of the time lag, we use the method of moment (MoM) estimator as initial estimator for the friction parameter of the maximum likelihood estimator (MLE). However, in several aspects the MLE is not robust. For robustification, we first derive elementary M-estimates by extending the method of M-estimation from Huber (1981). We use an intuitively robustified MoM estimate as initial estimate and compare by means of simulation the M-estimate with the MLE. This approach is, however, only ad-hoc since Huber’s minimum Fisher information and minimax asymptotic variance theory remains incomplete for simultaneous location and scale, and does not cover more general models (as for example the Ornstein–Uhlenbeck process). A more general robustness concept due to Kohl et al. (2010), Rieder (1994), and Staab (1984) is based on local asymptotic normality (LAN), asymptotically linear (AL) estimates, and shrinking neighborhoods. We then apply this concept to the Ornstein–Uhlenbeck process. As a measure of robustness, we consider the maximum asymptotic mean square error (maxasyMSE), which is determined by the influence curve (IC) of AL estimates. The IC represents the standardized influence of an individual observation on the estimator given the past. For two kind of neighborhoods (average and average square neighborhoods) we obtain optimally robust ICs. In case of average neighborhoods, their graph exhibits surprising, redescending behavior. For average square neighborhoods the graph is between the one of the elementary M-estimates and the MLE. Finally, we discuss the estimator construction, that is, the problem of constructing an estimator from the family of optimal ICs. We carry out in our context the One-Step construction dating back to LeCam and use both an intuitively robustified MoM estimate and the elementary M-estimate as initial estimate. This results in optimally AL estimates (for average and average square neighborhoods). By means of simulation we then compare the different estimators: MLE, elementary M-estimates, and optimally AL estimates. In addition, we give an application to electricity prices.

EPFL2012

Robust parameter estimation for the Ornstein-Uhlenbeck process

Sonja Rieder

In this paper, we derive elementary M- and optimally robust asymptotic linear (AL)-estimates for the parameters of an Ornstein-Uhlenbeck process. Simulation and estimation of the process are already well-studied, see Iacus (Simulation and inference for stochastic differential equations. Springer, New York, 2008). However, in order to protect against outliers and deviations from the ideal law the formulation of suitable neighborhood models and a corresponding robustification of the estimators are necessary. As a measure of robustness, we consider the maximum asymptotic mean square error (maxasyMSE), which is determined by the influence curve (IC) of AL estimates. The IC represents the standardized influence of an individual observation on the estimator given the past. In a first step, we extend the method of M-estimation from Huber (Robust statistics. Wiley, New York, 1981). In a second step, we apply the general theory based on local asymptotic normality, AL estimates, and shrinking neighborhoods due to Kohl et al. (Stat Methods Appl 19:333-354, 2010), Rieder (Robust asymptotic statistics. Springer, New York, 1994), Rieder (2003), and Staab (1984). This leads to optimally robust ICs whose graph exhibits surprising behavior. In the end, we discuss the estimator construction, i.e. the problem of constructing an estimator from the family of optimal ICs. Therefore we carry out in our context the One-Step construction dating back to LeCam (Asymptotic methods in statistical decision theory. Springer, New York, 1969) and compare it by means of simulations with MLE and M-estimator.

Springer-Verlag2012

Study of physical phenomena of selective laser melting towards increased productivity

Christoph Van Gestel

This thesis presents an experimentally focused study of three of the key physical phenomena of the Selective Laser Melting (SLM) process. The SLM process increasingly gains momentum in industrial applications. As awareness of the process and its capabilities for the manufacturing of high performance objects increases, some of the current limitations of the process become more apparent. The SLM process is not a fully mature manufacturing process and there are still significant gains to be made in terms of material understanding, process optimization, final part prediction and quality control. The experimental study of the physical aspects contributes to the material understanding on a fundamental level but also provides the tools for facilitating future research. A link to practical applications is presented in a case study on evaluating the influence of beam movement patterns and on the identification of process instabilities for quality control. A broad literature survey covers the state of the art on improving the SLM process. The three physical phenomena which are the subject of this study are: the interaction of laser beam light with the material to be processed, the transport of heat within the material and the behavior of the molten material. These three phenomena take place under conditions which are unlike conventional production processes, in terms of energy intensity, thermal gradients and liquid (metal) dynamics. The laser-material interaction is characterized by the absorptance. An integrating sphere setup is integrated in an SLM machine to measure the absorptance of the material in the pristine and final state. Also the absorptance evolution is characterized. Results are available for Maraging 300 steel powder, silver powder and AlSi10Mg powder. The influence of surface oxidation and surface roughness is discussed. The heat transport within the powder bed is characterized by diffusion. The thermal diffusivity and thermal conductivity are measured using the flash method with a laser as the energy source. The implementation uses a special laser beam intensity profile and data processing by correlation with numerical simulations. Thermal diffusivity and conductivity values are included for Maraging 300 steel, silver and titanium. The results confirm the large difference between bulk and powder conductivity and the limited influence of the material type. The melt pool behavior is studied using an integrated coaxial vision system. This system uses a combination of sensors to monitor the radiation which is emitted by the melt pool and its surroundings. The implementation presents optimized timing and data processing protocols. The relation between the melt pool properties and the process stability is explained. A procedure is also developed for obtaining temperature field profiles of the melt pool.

EPFL2015

EPFL2012

Study of physical phenomena of selective laser melting towards increased productivity

Christoph Van Gestel

EPFL2015