Pooling or fooling? An experiment on signaling

We compare two zero-sum versions of the so called Chinos Game, a traditional parlour game played in many countries. In one version, which we call Preemption Scenario, the first player who guesses right wins the prize. In the alternative version, called the Copycat Scenario, the last player who guesses right wins the prize. While in the Preemption Scenario there is a unique and fully revealing equilibrium, in the Copycat Scenario all equilibria have first movers pool (i.e. hide) their private information. Our experimental evidence shows, however, that in the latter case early movers do not pool but try to fool, i.e. to “lie” by systematically distorting behavior relative to equilibrium play. In fact, doing so they benefit, although the resulting gains diminish as the game proceeds. This highlights the point that, as players adjust their behavior off equilibrium, they also attempt to exploit the induced strategic uncertainty whenever the game allows for this possibility.Financial support from the Spanish Ministry of Economic Development (ECO2014-52345-P and ECO2015-65820-P), Generalitat Valenciana (Research Projects Grupos 3/086) and Instituto Valenciano de Investigaciones Económicas (IVIE) is gratefully acknowledged

Pooling or Fooling? An Experiment on Signaling

This paper reports the evidence from an experiment which takes advantage of the rich informational structure of the so-called Chinos Game, a traditional parlour game played in many countries. In the experiment subjects receive a binary private signal and have to guess the sum of these signals. We compare two constant-sum versions of the Chinos Game. In one version, which we call Preemption Scenario, the first player who guesses right wins the prize. In the alternative version, called the Copycat Scenario, the last player who guesses right wins the prize. While it is straightforward to see that the Preemption Scenario has a unique and fully revealing equilibrium, in all the equilibria of the Copycat Scenario first movers optimally hide their private information. However, our experimental evidence shows that subjects \u201clie\u201d in the Copycat Scenario (i.e., systematically distort behavior relative to equilibrium play) and they are successful at doing it, despite that benefits from lying are diminishing as the game proceeds

Overexpression of CX3CR1 in Adipose-Derived Stem Cells Promotes Cell Migration and Functional Recovery After Experimental Intracerebral Hemorrhage

Stem cell therapy has emerged as a new promising therapeutic strategy for intracerebral hemorrhage (ICH). However, the efficiency of stem cell therapy is partially limited by low retention and engraftment of the delivered cells. Therefore, it’s necessary to improve the migration ability of stem cells to the injured area in order to save the costs and duration of cell preparation. This study aimed to investigate whether overexpression of CX3CR1, the specific receptor of chemokine fractalkine (FKN), in adipose-derived stem cells (ADSCs) can stimulate the cell migration to the injured area in the brain, improve functional recovery and protect against cell death following experimental ICH. ADSCs were isolated from subcutaneous adipose tissues of rats. ICH was induced by means of an injection of collagenase type VII. ELISA showed that the expression levels of fractalkine/FKN were increased at early time points, with a peak at day 3 after ICH. And it was found that different passages of ADSCs could express the chemokine receptor CX3CR1. Besides, the chemotactic movements of ADSCs toward fractalkine have been verified by transwell migration assay. ADSCs overexpressing CX3CR1 were established through lentivirus transfection. We found that after overexpression of CX3CR1 receptor, the migration ability of ADSCs was increased both in vitro and in vivo. In addition, reduced cell death and improved sensory and motor functions were seen in the mice ICH model. Thus, ADSCs overexpression CX3CR1 might be taken as a promising therapeutic strategy for the treatment of ICH

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation

Resampling-based inference for time series in the frequency domain

This dissertation focuses on resampling-type methods for complex structured data collected over time. In particular, a notoriously difficult problem from time series is considered regarding resampling inference in the frequency domain. Despite many developments over the past 25 years, existing bootstrap methods struggle with complicated variance structures that arise in such inference. The first project starts with a more basic form of resampling, called subsampling, which has been largely ignored in efforts to develop bootstrap for frequency domain inference. We show that, non-trivially, subsampling solves the general variance estimation problem in the frequency domain under far weaker conditions than any existing bootstrap. We then link subsampling to the current state-of-the-art bootstrap methods and show that subsampling is key to broadly expanding the application of such bootstraps. The subsampling work in the complicated context of frequency domain inference for time series also suggests a larger and broader potential for alternative statistics and bootstrap schemes for dependent data. With the frequency domain, in particular, all existing bootstraps involve resampling periodogram ordinates, which commonly encounter problems because the periodogram ordinates exhibit are not independent. Essentially, the bootstrap principle can break down, so that existing resampling plans cannot entirely re-create spectral statistics and finite-sample performance suffers for distributional approximation. Using a sub-data scale perspective, though, it is possible to re-imagine bootstraps and statistics in a way that breaks with the past. In this spirit, the second project combines resampling techniques, including empirical likelihood, to formulate a fundamentally new bootstrap method for time series in the frequency domain. The third project focuses on another important topic in the frequency domain, involving the interval estimation of spectral densities, where current methods cannot produce meaningful results in practice. In the latter project, we re-innovate a current frequency domain bootstrap method and combine its strength with empirical likelihood to develop a novel hybrid inference method. The hybrid method is proven to be valid under a wide range of time processes and also demonstrates promising numerical performance for interval estimation