Probability Model

This paper examines the impact of home country economic status on immigrant self-employment probability in the U.S. We estimate a probability model and find that, consistent across race, immigrants from developed countries are more likely to be self-employed in the U.S than are immigrants from developing countries. This result is unexpected given previous research which suggests that immigrants from countries with high levels of self-employment tend to be more involved in self-employment in the U.S. Developing countries on average have higher self-employment rates than do developed countries but our research shows that immigrants from developing countries have similar or lower self-employment probabilities relative to native born White Americans, whereas immigrant from developed countries have significantly higher self-employment probabilities relative to native born White Americans. We provide two potential explanations for this result. First, immigrants from developed countries ma...

This study examined 2349 advanced mathematics students from the Third International Mathematics and Science Study. Students were separated into two groups: those that scored above the international average of 501 and those who scored below. Logistic regression was then utilized to model the student data. Results indicate that students whose parents had less than a high school education were one-fourth as likely to score above the international average and students who were enrolled in advanced mathematics and physics courses were three times as likely to score above the international. A probability model is also discussed. he intense concern over the mathematics achievement levels of U. S. elementary and high-school students can be seen through the number of articles published (Baker, 1993a, 1993b; Bracey, 1991, 1992, 1993; Freudenthal, 1975, Rotberg, 1990; Stedman, 1994a, 1994b). A great deal of debate has centered on the fact that the U.S. scored lower than many countries on most ...

Unequal probability sampling without replacement with inclusion probabilities i π exactly proportional to a measure of size x known for each unit (denoted by PS π ) is extensively used in large-scale surveys, especially for the selection of primary sampling units in multi-stage sampling. For simplicity, we focus on unistage sampling from a finite population U of size N. The design-unbiased Horvitz-Thompson estimator ∑∈ = s i i i HT y Y π ˆ is used to estimate the total Y of a characteristic of interest y, where s

In this paper we analyze aspects of the intellectual property strategies of firms in the global cosmetics and toilet preparations industry. Using detailed data on all 4,205 EPO patent grants in the relevant IPC class between 1980 and 2001, we find that about 15 percent of all patents are challenged in EPO opposition proceedings, a rate about twice as high as in the overall population of EPO patents. Moreover, opposition in this sector is more frequent than in chemicals-based high technology industries such as biotechnology and ...

Here, we present the collision probability of the IEEE 802.15.4 slotted Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) mechanism over a real-time wireless sensor network (RT-WSN), also called a synchronous network system. The “backoff,” the random delay of CSMA/CA, can be used to create a probability model and to determine the average packet delay over RT-WSN. The proposed theoretical analysis model was nearly consistent with the simulation and with experimental results. This suggests that the slotted CSMA/CA mechanism cannot be applied effectively to RT-WSN because it cannot avoid a high collision rate with RT-WSN application requirements and, therefore, may waste a great deal of system bandwidth. We found that the packet collision rate increased up to 731% compared to an ideal model with more than 10 users within an RT-WSN. Without improvements in methodology, the high collision rate makes this slotted CSMA/CA mechanism unsuitable for RT-WSN applications.

The development of the rainfall occurrence model is greatly important not only for data-generation purposes, but also in providing informative resources for future advancements in water-related sectors, such as water resource management and the hydrological and agricultural sectors. Various kinds of probability models had been introduced to a sequence of dry (wet) days by previous researchers in the field. Based on the probability models developed previously, the present study is aimed to propose three types of mixture distributions, namely, the mixture of two log series distributions (LSD), the mixture of the log series Poisson distribution (MLPD), and the mixture of the log series and geometric distributions (MLGD), as the alternative probability models to describe the distribution of dry (wet) spells in daily rainfall events. In order to test the performance of the proposed new models with the other nine existing probability models, 54 data sets which had been published by several authors were reanalyzed in this study. Also, the new data sets of daily observations from the six selected rainfall stations in Peninsular Malaysia for the period 1975–2004 were used. In determining the best fitting distribution to describe the observed distribution of dry (wet) spells, a Chi-square goodness-of-fit test was considered. The results revealed that the new method proposed that MLGD and MLPD showed a better fit as more than half of the data sets successfully fitted the distribution of dry and wet spells. However, the existing models, such as the truncated negative binomial and the modified LSD, were also among the successful probability models to represent the sequence of dry (wet) days in daily rainfall occurrence.

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

The Buller region has been one of the most active seismic regions of New Zealand over historical time. In order to conduct Probabilistic Seismic Hazard Assessments for the region, definition and quantification of all seismic sources in the region capable of generating potentially damaging earthquakes must be carried out. Bayesian Inference is used to associate events in the historical record to identified faulting sources in the region. Magnitude- Frequency relations for these seismic sources are derived. Subsequent activity rates are compared to plate motion estimates of Seismic Moment release rate for the region in an attempt to quantify any departure from long-term activity rates. Future occurrence rates of events are estimated using both Poisson and Time-dependent probability models. Resulting Magnitude- Frequency relations for these sources are readily applicable to Probabilistic Seismic Hazard Analyses for the region.

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).

We consider numerical solutions of the Darcy and Buckley–Leverett equations for flow in porous media. These solutions depend on a realization of a random field that describes the reservoir permeability. The main content of this paper is to formulate and analyze a probability ...