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1.
Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time. 相似文献
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The voluntary nonprofit sector in Ireland has grown significantly in recent years. A related trend has been the growth of cross‐border (in effect, transnational) cooperation between voluntary organizations based in Northern Ireland and the Republic of Ireland. However, this development has posed a set of management issues in terms of structure, forms of governance and decision making, day‐to‐day project management, and sustainability. Cross‐border work has also meant that voluntary bodies confront a distinctive set of barriers in relation to political and ethnic conflict, social and cultural attitudes, and practical delivery. Strengthening of cooperation across borders will require greater planning, mainstream funding, focused management, diversification of services, and realistic expectations. 相似文献
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The Analysis of Verbal Behavior - Participants first became familiar with an image showing moderate symptoms of the skin cancer melanoma. In a generalization test, they indicated whether images... 相似文献
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Techniques used in variability assessment are subsequently used to draw conclusions regarding the “spread”/uniformity of data curves. Due to the limitations of these techniques, they are not adequate for circumstances where data manifest with multiple peaks. Examples of these manifestations (in three-dimensional space) include under-foot pressure distributions recorded for different types of footwear (Becerro-de-Bengoa-Vallejo et al., 2014; Cibulka et al., 1994; Davies et al., 2003), surface textures and interfaces designed to impact friction, and and and molecular surface structures such as viral epitopes (Torras and Garcia-Valls, 2004; Pacejka, 1997; Fustaffson, 1997). This article proposes a technique for generating a single variable – Λ that will quantify the uniformity of such surfaces. We define and validate this technique using several mathematical and graphical models. 相似文献
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Kimberly F. Sellers Derek S. Young 《Journal of Statistical Computation and Simulation》2019,89(9):1649-1673
While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks. 相似文献
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Many studies have sought to gauge the impact of population growth on economic growth. A well‐known stylized fact of this literature is that the estimated effects of population growth measures on economic growth are not robust, varying between being positive, negative, and insignificantly different from zero. The present study analyzes 471 statistical regressions from 29 prominent economic growth studies using meta‐regression analysis to identify the effect of alternative methodologies on key population growth results. This study finds that a broad set of methodological factors explains more than half of the variation in the population growth effects observed from this literature, including the types of variables used to measure population growth, the countries selected, the time frame of the analysis, and the nature of the control variables specified. The study also yields results that have implications for policymakers, especially insofar as several policy factors seem to influence the population change–economic growth nexus. Particularly strong is the evidence in support of the increasingly adverse effects of population growth in the post‐1980 period, suggesting that demographic issues should warrant greater attention than they currently receive from the policymaking community. 相似文献